The impacts of demand response participation in capacity markets

Lynch, Muireann Á.; Nolan, Sheila; Devine, Mel T.; O’Malley, Mark

doi:10.1016/j.apenergy.2019.05.063

Cited by 71 publications

(33 citation statements)

References 26 publications

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“…The household appliances are categorized in power elastic, time elastic, and essential appliances that are mostly shift-able based on grid condition, energy storage, energy trading, and real-time pricing. Authors in [20] focus on system adequacy by taking advantage of DR programs such as shifting the loads in high generation periods. They consider the energy market, reserve market, and quantity-based capacity market to make a competitive field for generators to maximize their profit.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequential Tasks Shifting for Participation in Demand Response Programs

et al. 2020

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In this paper, the proposed methodology minimizes the electricity cost of a laundry room by means of load shifting. The laundry room is equipped with washing machines, dryers, and irons. Additionally, the optimization model handles demand response signals, respecting user preferences while providing the required demand reduction. The sequence of devices operation is also modeled, ensuring correct operation cycles of different types of devices which are not allowed to overlap or have sequence rules. The implemented demand response program specifies a power consumption limit in each period and offers discounts for energy prices as incentives. In addition, users can define the required number of operations for each device in specific periods, and the preferences regarding the operation of consecutive days. In the case study, results have been obtained regarding six scenarios that have been defined to survey about effects of different energy tariffs, power limitations, and incentives, in a laundry room equipped with three washing machines, two dryers, and one iron. A sensitivity analysis of the power consumption limit is presented. The results show that the proposed methodology is able to accommodate the implemented scenario, respecting user preferences and demand response program, minimizing energy costs. The final electricity price has been calculated for all scenarios to discuss the more effective schedule in each scenario.

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Section: Literature Reviewmentioning

confidence: 99%

Sequential Tasks Shifting for Participation in Demand Response Programs

et al. 2020

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“…Demand response (DR) is a form of power supply and demand interaction based on the electricity price signal or the incentive mechanism wherein the user changes the inherent power consumption mode to change the power load [19]. According to the different response modes of users, DR can be divided into two types: price-based DR and incentive-based DR. Usually, incentive-based DR can be used as a direct scheduling resource to perform joint power dispatching optimization, but the response bias of price-based DR is greatly affected by the response elastic coefficient and the excitation level, and thus, direct scheduling decisions cannot be made.…”

Section: Flexibility Of Demand Responsementioning

confidence: 99%

Optimal Scheduling of a Multi-Energy Power System with Multiple Flexible Resources and Large-Scale Wind Power

Che

Lou

et al. 2019

Energies

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With the grid-connected operation of large-scale wind farms, the contradiction between supply and demand of power systems is becoming more and more prominent. The introduction of multiple types of flexible resources provides a new technical means for improving the supply–demand matching relationship of system flexibility and promoting wind power consumption. In this paper, multi-type flexible resources made up of deep peak regulation of thermal units, demand response, and energy storage were utilized to alleviate the peak regulation pressure caused by large-scale wind power integration. Based on current thermal plant deep peak regulation technology, a three-phase peak regulation cost model of thermal power generation considering the low load fatigue life loss and oil injection cost of the unit was proposed. Additionally, from the perspective of supply–demand balance of power system flexibility, the flexibility margin index of a power system containing source-load-storage flexible resources was put forward to assess the contribution from each flexibility provider to system flexibility. Moreover, an optimal dispatching model of a multi-energy power system with large-scale wind power and multi-flexible resources was constructed, aimed at the lowest total dispatching cost of the whole scheduling period. Finally, the model proposed in this paper was validated by a modified RTS96 system, and the effects of different flexibility resources and wind power capacity on the optimal scheduling results were discussed.

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“…where R IL ω,t denotes the interruptible load provided by users at time stage t in the wind power combination scenario ω in the real-time stage. θ represents the proportion of the maximum of interruptible load that can be provided by the power side to the total load [27]. (11) Real-time reserve service (only one in use at any given time)…”

Section: The Constraints Of the Day-ahead Stagementioning

confidence: 99%

“…Step 4 According to Equation (27), calculate the mean vector of each type and use the vector as the new cluster center.…”

Section: K-means Classification Based On Numerical Weather Predictionmentioning

confidence: 99%

Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption

Dong¹,

Anyuan²,

Liu³

et al. 2019

Sustainability

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Today, wind power is becoming an important energy source for the future development of electric energy due to its clean and environmentally friendly characteristics. However, due to the uncertainty of incoming wind, the utilization efficiency of wind energy is extremely low, which means the problem of wind curtailment becomes more and more serious. To solve the issue of wind power large-scale consumption, a two-stage stochastic optimization model is established in this paper. Different from other research frameworks, a novel two-side reserve capacity mechanism, which simultaneously takes into account supply side and demand side, is designed to ensure the stable consumption of wind power in the real-time market stage. Specifically, the reserve capacity of thermal power units is considered on the supply side, and the demand response is introduced as the reserve capacity on the demand side. At the same time, the compensation mechanism of reserve capacity is introduced to encourage generation companies (GENCOs) to actively participate in the power balance process of the real-time market. In terms of solution method, compared with the traditional k-means clustering method, this paper uses the K-means classification based on numerical weather prediction (K-means-NWP) scenario clustering method to better describe the fluctuation of wind power output. Finally, an example simulation is conducted to analyze the influence of reserve capacity compensation mechanism and system parameters on wind power consumption results. The results demonstrate that with the introduction of reserve capacity compensation mechanism, the wind curtailment quantity of the power system has a significant reduction. Besides, the income of GENCOs is gradually increasing, which motivates their enthusiasm to provide reserve capacity. Furthermore, the reserve capacity mechanism designed in this paper promotes the consumption of wind power and the sustainable development of renewable energy.

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The impacts of demand response participation in capacity markets

Cited by 71 publications

References 26 publications

Sequential Tasks Shifting for Participation in Demand Response Programs

Sequential Tasks Shifting for Participation in Demand Response Programs

Optimal Scheduling of a Multi-Energy Power System with Multiple Flexible Resources and Large-Scale Wind Power

Two-Stage Optimization Model for Two-Side Daily Reserve Capacity of a Power System Considering Demand Response and Wind Power Consumption

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