DG planning incorporating demand flexibility to promote renewable integration

Dang, Can; Wang, Xifan; Wang, Xiuli; Li, Furong; Zhou, Baorong

doi:10.1049/iet-gtd.2018.5648

Cited by 22 publications

(16 citation statements)

References 26 publications

(25 reference statements)

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“…The desire for a cleaner and more sustainable society has led to a profound revolution in the energy industry. In the past decade, the share of wind and solar generation has witnessed a rapid growth worldwide [1], and there are many studies focusing on further promoting the renewable penetration [2], [3]. However, the uncertainty and volatility of renewable plants require sufficient backup capacity and storage units to maintain a balance between the generation and load in real time, causing additional costs and operational risks.…”

Section: A Motivationmentioning

confidence: 99%

Supply Inadequacy Risk Evaluation of Stand-Alone Renewable Powered Heat-Electricity Energy Systems: A Data-Driven Robust Approach

Cao

Wei

Chen

et al. 2021

IEEE Trans. Ind. Inf.

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Integration of heat and electricity supply improves the overall energy efficiency and system operational flexibility. The renewable powered heat-electricity energy system is a promising way to set up residential energy supply facilities in remote areas beyond the reach of power system infrastructures. However, the volatility of wind and solar energy brings about the risk of supply inadequacy. This paper proposes a data-driven robust method to quantify two measures of such a risk in the stand-alone renewable powered heat-electricity energy system. The uncertainty of renewable generation is modeled through a family of ambiguous probability distributions around an empirical one based on the Wasserstein metric; then the probability of heat and electricity load shedding during a short period and related penalty cost are discussed. Through a polyhedral characterization of renewable power feasible region, the load shedding probability under the Wasserstein ambiguity set comes down to a linear program. With a piecewise linear optimal value function of the penalty cost, its expectation under the worstcase distribution in the Wasserstein ambiguity set also gives rise to a linear program. The proposed method requires moderate information on renewable generation and makes full use of available data, while sustains computational tractability. The evaluation result is robust against the inaccuracy of renewable power distributions. Case studies demonstrate the effectiveness of the proposed approach.

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Section: A Motivationmentioning

confidence: 99%

Supply Inadequacy Risk Evaluation of Stand-Alone Renewable Powered Heat-Electricity Energy Systems: A Data-Driven Robust Approach

Cao

Wei

Chen

et al. 2021

IEEE Trans. Ind. Inf.

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“…Demand-side flexibility (DSF) is defined as the capability of consumption modification in response to control signals. Possible sources of those control signals are external market signals (or penalty signals [9]) to smart meters or internal control signals from the home energy management system (HEMS). Two trends continue to drive the growth of demandside flexibility.…”

Section: Index Termsmentioning

confidence: 99%

Clustering-Based Penalty Signal Design for Flexibility Utilization

et al. 2020

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“…Nonnetwork solutions (NNSs) such as DR and temporary EES/DG are also considered 32 as promising alternatives to conventional network reinforcement. Finally, another study 35 proposes a DG planning model coordinating demand flexibility, in which the DG expansion plan and the behavior of consumers in DR programs are co-optimized for the highest social welfare and the optimal utilization of renewable generation. The philosophy of the approach in another study 33 consists in finding the "optimal level of demand" for each year at which the network should be upgraded using network solutions while procuring temporary NNSs to supply the excess demand above this level.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The cost of reinforcement per line and per hour of constraints enables selection of the feeders, where DR (solved with linear programming) and/or reconfiguration (exhaustive research) is implemented in the second stage of the algorithm to remove these constraints. Finally, another study 35 proposes a DG planning model coordinating demand flexibility, in which the DG expansion plan and the behavior of consumers in DR programs are co-optimized for the highest social welfare and the optimal utilization of renewable generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

Neto

Abaide

Miranda

et al. 2019

Int Trans Electr Energ Syst

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Summary This paper proposes a new probabilistic model for active low‐voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33‐Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid.

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DG planning incorporating demand flexibility to promote renewable integration

Cited by 22 publications

References 26 publications

Supply Inadequacy Risk Evaluation of Stand-Alone Renewable Powered Heat-Electricity Energy Systems: A Data-Driven Robust Approach

Supply Inadequacy Risk Evaluation of Stand-Alone Renewable Powered Heat-Electricity Energy Systems: A Data-Driven Robust Approach

Clustering-Based Penalty Signal Design for Flexibility Utilization

Load modeling of active low‐voltage consumers and comparative analysis of their impact on distribution system expansion planning

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