How should price-responsive electricity tariffs evolve? An analysis of the German net demand case

Pereira, Diogo Santos; Marques, António Cardoso

doi:10.1016/j.jup.2020.101079

Cited by 9 publications

(6 citation statements)

References 48 publications

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“…It is defined in [44] that 24 hours in a day is divided into three periods: low-demand load (0:00-8:00), medium-demand load (8:00-16:00) and highdemand load (16:00-24:00). (iii) Quartile range clustering: Pereira [45] determined these demands through quartile ranges. The quartile range is divided into three limits: the inferior limit, the middle limit or median, and the superior limit.…”

Section: ) Load Classification Methodologymentioning

confidence: 99%

“…The quartile range is divided into three limits: the inferior limit, the middle limit or median, and the superior limit. In [45], the first quartile defines the base level, the fourth quartile defines the peak level, and the interquartile range defines the intermediate level.…”

Section: ) Load Classification Methodologymentioning

confidence: 99%

“…Guo et al [44] classified daily demand based on specific periods in a day when certain levels of demand occur, creating a representative load curve for each period used in their case study. Pereira [45] determined these demands through quartile ranges and use this information for designing tariffs. Pereira [46] further improved the previous method by classifying daily demand by both level and period in a day.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Diewvilai,

Audomvongseree

2024

IEEE Access

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This paper proposes a method for generation expansion planning that incorporates full-year hourly multiple load levels balance constraints, providing sufficient flexibility to address load fluctuations and intermittency associated with variable renewable energy sources. Typically, ensuring that the generation system possesses enough flexibility to manage this intermittency involves considering the operational characteristics of generators within unit commitment constraints. However, to mitigate the substantial computational burden caused by the number and type of variables, various approximation techniques are often employed. Unfortunately, these techniques can introduce unrealistic elements into the problem. Instead of considering the operational characteristics of generators, this approach classifies the system's demand into three levels: base load, intermediate load, and peak load, using the proposed load classification method. The multiple load-level balance constraints are then applied to ensure that the capacity of generation units in each level is sufficient to meet their corresponding demand, with particular emphasis on matching fast-response generation units and their corresponding demand. The resulting generation expansion plan can be obtained with significantly reduced computational effort. The proposed load classification method and generation expansion planning approach have been tested using the latest power development plan of Thailand. Compared to another method that is not taken flexibility into account, 5 Gigawatts of fast-response generation capacity are selected instead of base load generation units. With the improved computational time achieved by the proposed generation expansion planning method, it can account for input data uncertainty by solving multiple generation expansion planning problems with varying input data and distinct individual probabilities. INDEX TERMS power generation planning, power generation reliability, generation expansion planning, renewable energy NOMENCLATURE A. ACRONYMS

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Section: ) Load Classification Methodologymentioning

confidence: 99%

Section: ) Load Classification Methodologymentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

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Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Diewvilai,

Audomvongseree

2024

IEEE Access

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“…Several factors affecting electricity demand and supply have been investigated in the literature [40,[98][99][100][101][102][103]:…”

Section: Drivers Of Electricity Prices and Electricity Consumptionmentioning

confidence: 99%

Electricity Markets during the Liberalization: The Case of a European Union Country

Bojnec

Križaj

2021

Energies

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This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

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“…Two types of policies were considered in this study: incentives and increasing electricity prices. At present, many countries and regions in the world have implemented the policy of "peak and valley electricity price" [44][45][46][47]; that is, the price ladders are set at different time periods to reduce the peak electricity consumption of residents and ease the pressure on power supply. Meanwhile, more and more studies and practices have proven that the use of energy-saving incentives can also greatly encourage active energy-saving behaviors [48][49][50][51][52][53].…”

Section: Energy-saving Policiesmentioning

confidence: 99%

Modeling the Household Electricity Usage Behavior and Energy-Saving Management in Severely Cold Regions

Song

2020

Energies

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Accurate simulation and prediction of occupants’ energy use behavior are crucial in building energy consumption research. However, few studies have focused on household energy use behavior in severely cold regions that have unique energy use patterns because of the low demand of cooling in summer and the use of central heating system in winter. Thus, we developed an agent-based model to simulate the household electricity use behavior in severely cold regions, according to data for Harbin, China. The model regards apartments, residents, household appliances, and energy-management departments as agents and generates the household electricity consumption with respect to time, temperature, and energy-saving events. The simulation parameters include basic information of the residents, their energy-saving awareness, their appliance use behaviors, and the impact of energy-saving management. Electricity use patterns are described by decision-making mechanisms and probabilities obtained through a questionnaire survey. In the end, the energy-saving effects of different management strategies are evaluated. The results indicate that the model can visually present and accurately predict the dynamic energy use behavior of residents. The energy-saving potential of household electricity use in severely cold regions is mainly concentrated in lighting and standby waste, rather than cooling and heating, since the cooling demand in summer is low and the heating in winter mainly relies on central heating system of the city, not on household electricity appliances. Energy-saving promotion can significantly reduce the amount of energy waste (41.89% of lighting and 97.79% of standby energy consumption), and the best frequency of promotional events is once every four months. Residents prefer incentive policies, in which energy-saving effect is 57.7% larger than that of increasing electricity prices. This study realized the re-presentation of the changes of energy consumption in a large number of households and highlighted the particularity of household energy-saving potential in severely cold regions. The proposed model has a simple structure and high output accuracy; it can help cities in severely cold regions formulate energy-saving management policies and evaluate their effects.

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How should price-responsive electricity tariffs evolve? An analysis of the German net demand case

Cited by 9 publications

References 48 publications

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Enhancing Generation Expansion Planning With Integration of Variable Renewable Energy and Full-Year Hourly Multiple Load Levels Balance Constraints

Electricity Markets during the Liberalization: The Case of a European Union Country

Modeling the Household Electricity Usage Behavior and Energy-Saving Management in Severely Cold Regions

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