A demand response system for wind power integration: greenhouse gas mitigation and reduction of generator cycling

Broeer, Torsten; Tuffner, Francis K.; Franca, Anaissia; Djilali, Ned

doi:10.17775/cseejpes.2018.00500

Cited by 20 publications

(10 citation statements)

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“…Regarding the GHG emissions associated with thermal generators on standby for backup, it was considered herein that all energy generation technologies (e.g., coal, gas, nuclear, wind, solar) must be supported by other generators with similar backup requirements. Therefore, the associated GHG emissions are minimal (National Renewable Energy Laboratory -NREL, 2013; Broeer et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

Greenhouse gas and energy payback times for a wind turbine installed in the Brazilian Northeast

Fonseca¹,

Carvalho²

2022

Front. Sustain.

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IntroductionGoing a step further than quantifying environmental impacts, establishing the environmental and energy payback times of a wind turbine can significantly impact the planning of a wind farm. This study applies the Life Cycle Assessment methodology to a wind turbine and verifies its environmental and energy payback times.MethodsThe Life Cycle Assessment was developed with the SimaPro software, using the Ecoinvent database and the IPCC 2013 GWP 100y and Cumulative Energy Demand environmental impact assessment methods. The Life Cycle Assessment considered the extraction of raw material, production of parts and pieces, transportation, assembly, use, and decommissioning. Besides the material composition of the wind turbine, meteorological data was also utilized to calculate wind electricity production in Northeast Brazil. The environmental analysis and data on energy production were used to calculate the time required to recoup the energy and emissions due to wind electricity compared to the emissions of the electricity grid.ResultsThe emission factor of wind electricity was 0.0083 kg CO2-eq/kWh, and the emissions associated with consumption of electricity from the Brazilian Electricity mix was 0.227 kg CO2-eq/kWh. Consideration of the energy consumed for the manufacture of the wind turbine yielded an energy payback of 0.494 years, and greenhouse gas accountancy led to a payback of 0.755 years.DiscussionThe results demonstrate that the payback periods are much lower than the lifetime of the wind turbine, highlighting the important role in addressing climate change and energy savings. The combination of Life Cycle Assessment and energy and environmental paybacks can be used to measure sustainability and deploy wind energy projects in locations with the shorter payback times.

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Section: Resultsmentioning

confidence: 99%

Greenhouse gas and energy payback times for a wind turbine installed in the Brazilian Northeast

Fonseca¹,

Carvalho²

2022

Front. Sustain.

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“…Moreover, climate change and the pressure on the electricity industry to reduce greenhouse gas emissions are severe challenges for the industry in the new environment [5]. Applying methods and measures that can overcome this dilemma of the electricity industry in critical and sensitive situations while not imposing higher costs than before is perhaps one of the sector's most critical challenges.…”

Section: A Aims and Backgroundmentioning

confidence: 99%

“…Besides, there are a variety of control techniques for optimal management [183]. (5) Non-dispatchable generation resources (NGR) Such generation resources generally do not participate much in DR schemes due to their lack of controllability and continuity of output. DGs such as PV and wind power are among these categories and are often used in participation with other DR programs [184].…”

Section: Dr Strategies In the Residential Sectormentioning

confidence: 99%

An Overview of Demand Response: From its Origins to the Smart Energy Community

Honarmand¹,

Hosseinnezhad

Hayes

et al. 2021

IEEE Access

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The need to increase network efficiency, enhance reliability, and reduce environmental effects, as well as advances in communication infrastructures, have led to demand response (DR) becoming an essential part of smart grid operation. DR can provide power system operators with a range of flexible resources through different schemes. From the operational decision-making viewpoint, in practice, each scheme can affect the system performance differently. Therefore, categorizing different DR schemes based on their potential impacts on the power grid, operational targets, and economic incentives can embed a pragmatic and practical perspective into the selection approach. In order to provide such insights, this paper presents an extensive review of DR programs. A goal-oriented classification based on the type of market, reliability, power flexibility and the participants' economic motivation is proposed for DR programs. The benefits and barriers based on new classes are presented. Every involved party, including the power system operator and participants, can utilize the proposed classification to select a proper plan in the DR-related ancillary service ecosystem. The various enabling technologies and practical strategies for the application of DR schemes in various sectors are reviewed. Following this, changes in the procedure of DR schemes in the smart community concept are studied. Finally, the direction of future research and development in DR is discussed and analyzed.

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“…One approach for GHG emission reduction can be seen in [17]. A small residential home region with a smart grid power was modelled and the impacts on GHG of adding both wind power and implementing demand response (DR) were explored.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration

2023

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An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this paper, we propose two ICA models incorporating energy‐limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimisation and considers GHG emission limits and maximises social welfare (SW). The second ICA model uses a fuzzy optimisation technique to simultaneously optimise the objectives of SW maximisation and GHG emission minimisation. Both ICA models are tested on two datasets with 10 and 338 capacity supply offers constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimisation is shown to find a better balance between maximising net SW and minimising GHG emissions, with superior reductions in GHG for minor decreases in SW. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.

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A demand response system for wind power integration: greenhouse gas mitigation and reduction of generator cycling

Cited by 20 publications

References 0 publications

Greenhouse gas and energy payback times for a wind turbine installed in the Brazilian Northeast

Greenhouse gas and energy payback times for a wind turbine installed in the Brazilian Northeast

An Overview of Demand Response: From its Origins to the Smart Energy Community

Fuzzy optimisation model of an incremental capacity auction formulation with greenhouse gas consideration

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