Future electricity market structure to ensure large volume of RES

Obushevs, Artjoms; Oļeiņikova, Irina; Syed, Mazheruddin H.; Zaher, Ammar; Burt, Graeme

doi:10.1109/eem.2017.7981972

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…Intelligent management is also deeply integrated with planning, construction, operation, and big data application. Meanwhile, it will provide strong support for the lean, intelligent and efficient operation of the company [25].…”

Section: Intelligent Managementmentioning

confidence: 99%

A Review of Smart Energy and Integrated Energy Service Business Model

Zhenguo¹,

Wang²,

Liu³

et al. 2020

Preprint

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Based on the high-potential 5G network and ubiquitous power Internet of things, the concept of smart energy has been proposed to solve the problem of optimal utilization of the power system's generation, transmission, distribution, consumption, and the related services. Therefore, innovation and reformation will be doomed and embraced by smart energy in China's energy system, which includes technological progress and system mechanism reform. In the meanwhile, new solutions and challenges would be provided by smart energy for the State Grid Corporation of China's business model. Focused on the analysis of smart energy marketing strategy and profit model, this paper lists the business model and scenarios under the developing tendency of smart energy and provides a detailed sorting and analysis for the smart operation and management of user service and ecological platform. This article aims to analyze the value of smart energy and provides possible business model choices for integrated energy suppliers.

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Section: Intelligent Managementmentioning

confidence: 99%

A Review of Smart Energy and Integrated Energy Service Business Model

Zhenguo¹,

Wang²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…where AR is the area of the rotor, U0 is the free-stream velocity, and CP is the power coefficient for a wind turbine defined as a function of the axial induction factor 4a(1 − a) 2 . The CP values applicable for most wind turbine applications are associated with axial induction factors between 0 and 0.5.…”

Section: Theoretical Modelling Of Power Generation Devicesmentioning

confidence: 99%

“…The total portfolio of Distributed Energy Resources (DER) including renewable energy sources is expected to surpass traditional forms of generation in 2018 [1]. Renewable Energy Sources (RES) have high output instability due to time and environmental influences, and it is difficult to predict the power generation output [2]. It is valuable to obtain the optimum capacity of renewable energy supply systems for specified load characteristics.…”

Section: Introductionmentioning

confidence: 99%

Economic Evaluation of Renewable Energy Systems for the Optimal Planning and Design in Korea – A Case Study

Chung

Shin

Jeoune³

et al. 2018

J. sustain. dev. energy water environ. syst.

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In this study, an engineering economics software was developed to find the optimum design of renewable energy systems in micro-grid, off-grid, and on-grid cases. The software was used to calculate the renewable energy production from photovoltaic and wind turbines in local regions in Korea. TMY2 files were made for the renewable energy calculation based on meteorological data from 28 locations in Korea. To calculate the output of the photovoltaic and the wind turbine, a power generation model based on theoretical equations was developed. A fuel cell and diesel engine generator can be selected to use as a base power source model, and a battery energy storage device was included for efficient energy management. To verify the results of renewable power generations from solar and wind, the models were compared with commercial programs such as TRNSYS and HOMER. The results showed good agreement under the same conditions. A sensitivity analysis for a grid-connected factory in a specific region was carried out and the optimum investment design condition for a renewable energy system to reduce costs through peak load reduction was calculated. The sensitivity analysis for off-grid and grid-connected system designs was also performed and it will be continued to improve the accuracy of the model by comparing the data with that from a demonstration complex. The developed program can provide useful information for investment decisions through the optimal design of facility capacity and economic analysis when investing in renewable energy supply facilities.

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“…Currently the price of energy is the result of a complex market structure [1], [2]. The liberalization of the electricity sector resulted in many different actors playing various roles in providing energy to end users.…”

Section: Introductionmentioning

confidence: 99%

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

et al. 2021

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In a highly competitive and liberalized energy market, where the retail of electricity is open to many potential companies, it is essential to have tools that help make decisions and guide the design of marketing strategies. In this sense, it is essential for retailers to know the behavior of their customers to correctly define their commercial strategies. One of the most commonly used methods for this is the characterization of their consumption profiles. Fortunately, for regulatory reasons, in some countries, the monthly electricity demand of each customer is openly available to any competitor. This paper explores whether this information, especially the economic sector and geographic location of a client, is useful for determining the client's demand profile. Specifically, data on electricity demand in Spain from more than 27 million users and for a period of 3 years are analyzed. For this purpose, the electricity consumption of every client is grouped by month and normalized. The resulting demand profiles are later clustered according to different criteria. The main finding of the research is that the combined information on economic activity and location definitely enables prediction of the demand profile. Additionally, profile quality metrics are defined and obtained for the entire dataset. The resulting profiles have a mean dispersion of 10% and a confidence interval of ±17%. To clarify the use of these metrics, several examples are detailed, showing how this profile information can be used to improve the marketing decision-making process for electricity retailers.INDEX TERMS Energy demand, customer profiling, data engineering, big data applications, statistical learning, pattern analysis.

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Future electricity market structure to ensure large volume of RES

Cited by 7 publications

References 6 publications

A Review of Smart Energy and Integrated Energy Service Business Model

A Review of Smart Energy and Integrated Energy Service Business Model

Economic Evaluation of Renewable Energy Systems for the Optimal Planning and Design in Korea – A Case Study

Monthly Electricity Demand Patterns and Their Relationship With the Economic Sector and Geographic Location

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