Optimal Strategy of Active Distribution Network Considering Source–Network–Load

Kong, Xiangyu; Yong, Chengsi; Wang, Chengshan; Chen, Ying; Yu, Li

doi:10.1049/iet-gtd.2018.5781

Cited by 10 publications

(8 citation statements)

References 31 publications

(33 reference statements)

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“…For instance, ref. [10] presents an optimal operation model considering the active managing costs of the source–network–load and the site selection and the incentive price for the responsive load. Further, a bi‐level optimization model is proposed in [11] for the strategic participation of active distribution networks to provide energy and reserves in wholesale electricity markets.…”

Section: Introductionmentioning

confidence: 99%

Managing reserve deliverability risk of integrated electricity‐heat systems in day‐ahead market: A distributionally robust joint chance constrained approach

Chen

Wang

et al. 2023

IET Generation Trans & Dist

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The integrated electricity and heat system (IEHS) is an emerging demand‐side flexible resource for power systems. IEHS operators participating in electricity markets considering their capabilities in reserve provision will face the reserve deliverability risk due to the energy‐limited storage nature of heat systems. To address this challenge and increase profitability, a distributionally robust joint chance‐constrained mechanism with enhanced quantifications is adopted for the heating system and reserve deployment uncertainties. Detailed pipeline storage representation for thermal networks and integrated demand response are incorporated into this strategic participation model. A two‐stage distributionally robust joint chance constrained program is then incorporated to effectively manage the reserve deliverability risk by addressing uncertainties from local distributed energy resources and real‐time reserve requests. The L‐shaped algorithm is then customized by incorporating bi‐linear Benders’ decomposition and modified scenario filtering method to efficiently tackle solution challenges for the sophisticated model. Numerical results show the advantages of our approach in virtual thermal storage utilization, risk management, computational performance enhancement and scalability.

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Section: Introductionmentioning

confidence: 99%

Managing reserve deliverability risk of integrated electricity‐heat systems in day‐ahead market: A distributionally robust joint chance constrained approach

Chen

Wang

et al. 2023

IET Generation Trans & Dist

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“…There were some studies on the energy management framework of ADNs. Kong et al (2019) constructed a twolayer distributed optimization model for ADNs and MG with different interests, in which one layer is the active judgment modules and another layer is the active control modules. The active judgment modules included power flow calculation, voltage quality judgment, and supply margining judgment.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dispatching Strategy of Active Distribution Network for Promoting Local Consumption of Renewable Energy

Xie

Wang

Wang³

et al. 2022

Front. Energy Res.

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Large-scale renewable energy sources (RESs) have been integrated into the active distribution network (ADN). For promoting the local consumption of RESs within ADN, an optimal dispatching strategy was proposed with two-stage hierarchical energy management framework. On the spatial boundary, a two-layer energy management framework was designed with the local optimization layer and the global optimization layer. The local optimization layer was for optimal power flow in the branch feeder with the objective functions of minimizing operation costs and maximizing the consumption of RESs. The global optimization layer was for optimal power flow in the main feeder with the objective functions of minimizing power loss and the voltage deviation of nodes. On the time scale, two-stage optimal dispatching models were established, including the day-ahead optimal models and intra-day optimal models. The day-ahead optimal models identified the operation status of the controllable units, and then the intra-day optimal models were updated with the ultra-short-term forecast results. A risk indicator was introduced to quantify the uncertainty of RES, and a non-dominated sorting genetic algorithm with elite strategy was adopted to solve the multi-objective nonlinear programming problem. An actual project in northern China was used as the testing system. The results of case studies verify that the proposed strategy can effectively realize the maximum local consumption of RESs and support the economic operation of ADN.

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“…Demand response controllability was investigated in [18] for the unit commitment model with limited predictability and residential DR resources. By considering the site selection and the incentive price, an optimal strategy for responsive loads was proposed in [19] for source-network-load system. Online demand response for non-deferrable loads was investigated in [20] with rechargeable battery and renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

Hu¹,

Cao²

2021

Preprint

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Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

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Optimal Strategy of Active Distribution Network Considering Source–Network–Load

Cited by 10 publications

References 31 publications

Managing reserve deliverability risk of integrated electricity‐heat systems in day‐ahead market: A distributionally robust joint chance constrained approach

Managing reserve deliverability risk of integrated electricity‐heat systems in day‐ahead market: A distributionally robust joint chance constrained approach

Optimal Dispatching Strategy of Active Distribution Network for Promoting Local Consumption of Renewable Energy

Chance-Constrained Optimization Dispatch and Demand Response Control of Thermostatically Controlled Loads and Plug-in Electric Vehicles

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