Technical, Economic, Social and Regulatory Feasibility Evaluation of Dynamic Distribution Tariff Designs

Christensen, Kristoffer; Ma, Zheng; Jôrgensen, Bo Nørregaard

doi:10.3390/en14102860

Cited by 11 publications

(6 citation statements)

References 40 publications

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“…Furthermore, each dimension consists of several sub-dimension and macro and micro levels (as shown in Table 7). Various energy ecosystem cases have applied the CSTEP, e.g., microgrids (Ma et al 2018b) and distribution tariffs (Christensen et al 2021).…”

Section: Business Ecosystem Dimensionsmentioning

confidence: 99%

“…Therefore, a feasibility assessment needs to be conducted and identify the most feasible solutions that potentially can be applied in the targeted ecosystem. One method can be applied with modification is the feasibility assessment method applied (Christensen et al 2021).…”

Section: Future Ecosystemmentioning

confidence: 99%

“…According to Christensen et al 2020d, the EV charging strategies can be categorized as centralized and decentralized, and the decentralized charging strategies are usually used by the EV users. Furthermore, based on evaluation with the modified feasibility assessment method (Christensen et al 2021), Real Time Pricing (Nimalsiri et al 2019), Time-of-Use Pricing (Chunlin et al 2017), and Timed charging (Huachun et al 2012) are the most feasible decentralized EV charging strategies in Demark.…”

Section: Potential Solution Identificationmentioning

confidence: 99%

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Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

Christensen

Rasmussen

et al. 2022

Energy Inform

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Understanding the local needs and challenges is critical for technology adoption in the energy sector. However, it is still a big challenge for most ecosystem stakeholders. Furthermore, technology adoption theories have mainly focused on the technology itself, and the business ecosystem perspective has been neglected. Therefore, this paper proposes an ecosystem-driven business opportunity identification method, a systematic approach for ecosystem stakeholders to conduct business opportunity analysis and evaluation based on the CSTEP ecosystem analysis and evaluation method. This method includes four correlated steps: Step 1: Identify the five CSTEP dimensions of the business ecosystem; Step 2: Identify potential changes in the business ecosystem; Step 3: Identify future ecosystem trends and timeline; Step 4: Select business opportunities; and Step 5: Potential solution identification. A web-based tool called opportunity identifier is developed for implementing the proposed method. A case study of the electric vehicle (EV) home charging energy ecosystem in Denmark is applied and demonstrates the application of the proposed method and the implementation of the developed web-based tool. Three value propositions are identified in the case study: (1) EV users can have optimal EV charging cost and optimal CO2 emission consumption with the intelligent EV charging algorithms that consider electricity prices, tariffs, and CO2 emission; (2) DSOs can avoid grid overloads and postpone the grid upgrade by applying intelligent EV charging algorithms; (3) Independent aggregators can aggregate EVs and participate in the ancillary service market or provide Vehicle-to-Grid services by using intelligent EV charging algorithms. Moreover, three feasible decentralized EV charging strategies (Real Time Pricing, Time-of-Use Pricing, and Timed charging) are identified as the potential solutions targeting the first value proposition.

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Section: Business Ecosystem Dimensionsmentioning

confidence: 99%

Section: Future Ecosystemmentioning

confidence: 99%

Section: Potential Solution Identificationmentioning

confidence: 99%

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Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

Christensen

Rasmussen

et al. 2022

Energy Inform

Self Cite

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“…Compared to large-scale battery, demand response is a better solution (Ma & Jørgensen, 2018). However, demand response might create enormous active activities in the grid due to the communication between the electricity markets and the demand sides, which potentially affects the distribution grid stability (Christensen et al, 2021). Therefore, accurate predictions of the future loads in the distribution grid are necessary.…”

Section: Introductionmentioning

confidence: 99%

A scoping review of deep neural networks for electric load forecasting

Vanting

Jôrgensen

2021

Energy Inform

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The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts; households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible.

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“…In [19], it is proposed a robust DT method for day-ahead congestion management which is capable of dealing with forecasting errors. Finally, several promising studies covering a variety of RTP approaches developed for specific country scenarios can be found in [20]- [24].…”

Section: Introductionmentioning

confidence: 99%

Self-Sustainable Dynamic Tariff for Real Time Pricing-Based Demand Response: A Brazilian Case Study

Schumacher¹,

Lachovicz

Macedo

et al. 2021

IEEE Access

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The operation of power systems under peak demand conditions results in higher operational costs, more instability risks and often forces an excessive use of fossil fuels. In this context, this paper proposes a self-sustainable real-time pricing (RTP) tariff that is adjusted proportionally to the aggregated demand of a group of consumers, and is based on a changing rate for each hour of the day. Therefore, demand peaks are expected to be naturally reduced by the group due to the higher tariff values imposed during these operating conditions. In addition, consumers benefit from the opportunity to reduce their bills according to the tariff and can carry out financial strategies by setting up their own consumption profile. The proposed dynamic tariff has a self-sustainable and revenue neutral nature which protects both consumers and distribution power companies from being economically affected by varying price elasticity scenarios. This is achieved by mathematically guaranteeing for such companies a revenue equal to the one they would expect with the traditional application of flat tariff. The advantages of the proposal are highlighted through numerical simulations by comparing it with the so-called Conventional Tariff and White Tariff, which are currently adopted by residential consumers in Brazil.

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Technical, Economic, Social and Regulatory Feasibility Evaluation of Dynamic Distribution Tariff Designs

Cited by 11 publications

References 40 publications

Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark

A scoping review of deep neural networks for electric load forecasting

Self-Sustainable Dynamic Tariff for Real Time Pricing-Based Demand Response: A Brazilian Case Study

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