Coordinated optimal dispatch of VPPs in unbalanced ADNs

Xu, Tong; Wu, Wenchuan; Zhenyi, Wang; Zhu, Tao

doi:10.1049/iet-gtd.2019.0697

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…Statistically, the mathematical optimization methods related to microgrids or VPPs mainly include linear programming (LP) [15], mixed integer linear programming (MILP) [16], non-linear programming (NLP) [17], mixed integer non-linear programming (MINLP) [18], quadratic programming [19], decision tree [20], interior point method [21], dynamic programming [22], game theory [23], fuzzy simulation [24], point estimate method [25], etc. LP and MILP are the most classic methods for solving the scheduling problems of both microgrids and VPPs.…”

Section: Mathematical Methodsmentioning

confidence: 99%

“…Xu et al. employed the primal‐dual interior‐point algorithm to transform a distributed optimal dispatch model of VPPs into a Karush–Kuhn–Tucker equation, and developed the distributed quasi‐Newton method [21]. Wu et al.…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al employed the primal-dual interior-point algorithm to transform a distributed optimal dispatch model of VPPs into a Karush-Kuhn-Tucker equation, and developed the distributed quasi-Newton method[21].Wu et al established a Stackelberg game model for the risk cost model of VPPs in electricity markets to reduce the bidding bias of electricity trading…”

mentioning

confidence: 99%

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Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Tan

Shen

et al. 2022

IET Renewable Power Gen

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To realize the national strategies for carbon emission reduction, electric power industries should undergo reforms to cope with the multiple challenges of decarbonization, marketization, and energy transition. How to design a dispatch strategy that considers both low‐carbon demand and economic cost has become a major concern in integrated energy systems. To realize multi‐energy complementation and low‐carbonization, a scheduling framework of combined heat and power‐virtual power plants (CHP‐VPPs) considering carbon capture and power‐to‐gas is proposed. The stochastic dynamic optimization problem is modelled as a Markov decision process. An improved deep reinforcement learning‐based algorithm named multi‐level backtracking prioritized experience replay‐twin delayed deep deterministic policy gradient (MBEPR‐TD3) is employed to solve the low‐carbon economic dispatch problem. The results show that: (1) The profits have been increased by 85.8% and carbon emissions have been reduced by 30.3% because of the addition of power‐to‐gas in CHP‐VPP; (2) the revenues have been improved by 22.24% and carbon emissions have been decreased by 37.04% owing to the introduction of carbon capture and trading; (3) compared with results using DQN, DDPG, and TD3, the optimal dispatch strategy obtained by MBEPR‐TD3 has higher reward returns and more stable convergence.

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Section: Mathematical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Tan

Shen

et al. 2022

IET Renewable Power Gen

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“…These activities of market participation and system management and support for VPP are considered respectively as commercial and technical activities. Thus the concepts appear as commercial VPP (CVPP) and technical VPP (TVPP) [4], [5]. The functionality of CVPP mainly includes (i) trading in the wholesale energy market, (ii) balancing of trading portfolios and the provision of services that are not location-specific to the system operator.…”

Section: Introductionmentioning

confidence: 99%

Role of Aggregator in Coordinating Residential Virtual Power Plant in “StoreNet” : A Pilot Project Case Study

Bahloul

Breathnach

Cotter³

et al. 2022

IEEE Trans. Sustain. Energy

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Towards the development and demonstration of an innovative business model where the value proposition for consumers/prosumers, aggregators and network operators are well maintained, this study assesses the performance of different aggregation control strategies for a distributed energy storage based residential virtual power plant (VPP). A special focus is given on the social welfare and network strength and their relation to energy storage capacity and power budgets allocation. The study is based on a real-life demonstration project, StoreNet where the basic self-consumption (SB-SC) control strategy has already been deployed. Analysing one-year measured data, it is observed that the implemented SB-SC approach allows 16%-19% electricity cost-saving, whereas the proposed VPP-bill minimisation approach can benefit from 37%-42% cost saving. This is also 7%-8% higher than the single home bill minimisation approach where the community does not participate in the VPP model. In contrast, the peak shaving approach is more favourable for the network operator. It reduces the load peak by 46.5%-64.7% and also drastically reduces the benefits for the customers and aggregator. Based on these studies and learning, some recommendations are made addressing the integration aspect of residential VPP and the future development of this concept for the local and wholesale energy markets.

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“…According to [8], there is no widely adopted VPP definition. In [9], VPP is defined as a combination of micro-units that are connected to the low-voltage distribution system.…”

Section: Introductionmentioning

confidence: 99%

A MILP-Based Approach for Virtual Microgrid Restoration

2020

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There has been a sharp increase in the demand for energy in recent years as a result of technological and societal advances, requiring continuous updates to the power system networks. Greater reliance on renewable energy (RE) sources is seen as a viable way to minimize the dependency on conventional sources, which also plays a significant role in increasing system reliability and reducing the environmental pollution. The system resulting from merging RE sources with the distribution system (DS) should have the ability to isolate and restore the power when faults occur. In this paper, we propose a smart restoration technique for the virtual microgrid networks. The motivation of the proposed technique was derived from merging the concept of fault management in the microgrid system and participating in the energy market (EM) in virtual power plant (VPP) networks, and the technique is referred to as the virtual microgrid system. Several factors are considered in the study, including the maximum load demand, the available power supply, and the operation cost. The restoration problem is posed as a Mixed Integer Linear Programming (MILP) problem. The system is implemented as an IEEE 13 bus system and it is assumed to be a smart virtual microgrid system that consists of automated devices, such as automated switches, computer-based remote control, and sensors. INDEX TERMS Distribution system (DS), energy market (EM), mixed-integer linear programming (MILP), renewable energy (RE), virtual power plant (VPP).

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Coordinated optimal dispatch of VPPs in unbalanced ADNs

Cited by 15 publications

References 33 publications

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Low‐carbon economic dispatch of the combined heat and power‐virtual power plants: A improved deep reinforcement learning‐based approach

Role of Aggregator in Coordinating Residential Virtual Power Plant in “StoreNet” : A Pilot Project Case Study

A MILP-Based Approach for Virtual Microgrid Restoration

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