Co-Planning of Demand Response and Distributed Generators in an Active Distribution Network

Yu, Yi; Wen, Xiaoru; Zhao, Jian; Xu, Zhao; Li, Jiayong

doi:10.3390/en11020354

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…In [6], several load levels were contained in the proposed DR (P-DR) model, while PDR and energy storage systems were treated as equals in the expansion planning model. In [7], co-planning of DG and DR equipment was carried out, where PDR was described by a linearized price-elastic DR model. The results showed that co-planning of DG and DR can minimize system costs as well as operation violations.…”

Section: A Indices δ Errsocrmentioning

confidence: 99%

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Gao

Tian³

et al. 2021

Journal of Modern Power Systems and Clean Energy

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Demand response, the reactive power output of distributed generation (DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG. With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

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Section: A Indices δ Errsocrmentioning

confidence: 99%

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Gao

Tian³

et al. 2021

Journal of Modern Power Systems and Clean Energy

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“…Introducing a high penetration of DPVSs to the grid could have significant impacts on many aspects, including demand response (DR) capacity estimation [3][4][5][6], customer baseline load estimation [7,8], load forecasting [9,10] and distribution network planning [11,12]. Specifically, in terms of DR capacity estimation, the DR aggregators need to estimate how much DR capacity they have in order to formulate the reasonable bidding strategy in the electricity market.…”

Section: Background and Motivationmentioning

confidence: 99%

A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Wang

et al. 2018

Energies

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Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

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“…However, as the proportion of Renewable Distributed Generation connected to the distribution network, the economy and stability of the distribution network are particularly important [1][2][3][4]. With the large-scale access of DG, the traditional centralized management mode of distribution network is not applicable, and a new load scheduling method for demand side load is needed [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Two-Layer Optimal Dispatching Strategy of Distribution Network Considering Demand Side Load

Wang,

Zhu,

Zhou

2024

Frontiers in Artificial Intelligence and Applications

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The traditional optimal dispatching of distribution network usually takes the demand load measurement as the rigid load. However, as the demand for electricity continues to rise, a large amount of distributed renewable energy generation is incorporated into the power grid. It is difficult to ensure the safe and reliable operation of the electrical system if only the optimal dispatching of the generating side is carried out. This paper proposes a two-layer optimal distribution network scheduling strategy that considers the demand side load. The upper layer model chooses controllable load output as the objective and aims to achieve the lowest overall operation cost of the distribution network, as well as the lowest comprehensive load fluctuation to reflect the power supply reliability of the distribution network. The adaptive particle swarm optimization algorithm based on genetic algorithms is implemented to solve the model based on its characteristics. Simulation verification is performed on the IEEE33-node distribution system, and the results depict that the proposed two-layer optimization method reduces the operating cost of the distribution network, effectively stabilizes the peak valley difference of load, and improves the overall stability of the distribution network.

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Co-Planning of Demand Response and Distributed Generators in an Active Distribution Network

Cited by 10 publications

References 31 publications

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Two-Layer Optimal Dispatching Strategy of Distribution Network Considering Demand Side Load

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