Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method

Zhou, Yong‐Wu; Guo, Shengkai; Xu, Fei; Dai, Chao; Ge, Wei; Chen, Xiaodong; Gu, Bing-Lin

doi:10.3390/en13071599

Cited by 7 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The heating demand of the Chinese "Three North Regions" in the heating season is mainly met by Combined Heat and Power (CHP), but due to its unique operation mode of "setting electricity by heat," the space for wind power to be connected to the Internet in the heating season is limited. This greatly limits the consumption of wind power, and the thermal power unit will generate a large amount of CO 2 in the process of operation (Zhou et al, 2020), which is not conducive to the realization of the goal of "carbon peaking and carbon neutrality" in China. In order to improve the on-grid rate of wind power in the power system, it is necessary to perform a thermodecoupling operation on the CHP to reduce the forced output of the unit.…”

Section: Introductionmentioning

confidence: 99%

Optimization method of wind power consumption based on thermal storage tanks against the background of stepped carbon trading

et al. 2023

View full text Add to dashboard Cite

In the context of Chinese efforts to achieve the goal of “carbon peaking and carbon neutrality” for the “Three North” area heating season, thermal power units, due to their unique operation mode of “fixing electricity by heat” and the resulting problem of insufficient wind power consumption by the night system, were proposed as an optimization method for wind power consumption based on thermal storage tanks in the context of stepped carbon trading. The optimization method is applied to thermal power plants and considers the constraints of cogeneration unit thermoelectric coupling, electrical power balance, and thermal power balance. Taking the lowest total cost of thermal power plants as the objective function, the Cplex solver in Matlab is used to solve the objective function. The wind power consumption by the system, the CO2 emissions of thermal power plants, and the economy of the system are analyzed in different scenarios. The calculation example results show that the model proposed in this article not only improves the wind power consumption rate of the system and reduces the CO2 emission of the thermal power plant but also improves the economy.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization method of wind power consumption based on thermal storage tanks against the background of stepped carbon trading

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Because of the current sensitivity method [11,12] with the relatively high computational efficiency which ignores the effect of reactive power, the obtained system correction adjustment scheme with UPFC may cause a secondary overload of the bus voltage when eliminating line overload and hence, bring new safety risks to the system, which is are, to be sure, not practically useful. Therefore, the current security correction problem of system with UPFC still needs to adopt the model-based optimization method [13][14][15], that is, to use the mathematical analysis method to solve the optimization model and obtain corresponding adjustment measures. It has been established in [16] that a security correction model with the minimum adjustment amount and uses the distributed robust optimization method to handle the power system security correction control problem with the uncertainty of wind power output; It has also been revealed in [17] that an optimization model considering the series compensator with the minimum load removal as the objective function.…”

Section: Introductionmentioning

confidence: 99%

A Data Driven Security Correction Method for Power Systems with UPFC

Li¹,

Zhang²,

Zhou³

et al. 2023

Energy Engineering

View full text Add to dashboard Cite

The access of unified power flow controllers (UPFC) has changed the structure and operation mode of power grids all across the world, and it has brought severe challenges to the traditional real-time calculation of security correction based on traditional models. Considering the limitation of computational efficiency regarding complex, physical models, a data-driven power system security correction method with UPFC is, in this paper, proposed. Based on the complex mapping relationship between the operation state data and the security correction strategy, a two-stage deep neural network (DNN) learning framework is proposed, which divides the offline training task of security correction into two stages: in the first stage, the stacked auto-encoder (SAE) classification model is established, and the node correction state (0/1) output based on the fault information; in the second stage, the DNN learning model is established, and the correction amount of each action node is obtained based on the action nodes output in the previous stage. In this paper, the UPFC demonstration project of Nanjing West Ring Network is taken as a case study to validate the proposed method. The results show that the proposed method can fully meet the real-time security correction time requirements of power grids, and avoid the inherent defects of the traditional model method without an iterative solution and can also provide reasonable security correction strategies for N-1 and N-2 faults.

show abstract

“…The authors of [22] provided a two-stage scheduling approach on how to coordinate flexibility and efficiency in the operation of CEWS. Considering the wind power uncertainty, [23] discussed both the day-ahead and intra-day optimization scheduling methods for CEWS in consideration of the heat storage capacity of the hot water system. The authors of [24] explored whether the accuracy of the simulation model will have a substantial impact on the operation cost for a CCHP system.…”

Section: Introductionmentioning

confidence: 99%

A Novel Steady-State Simulation Approach for a Combined Electric and Steam System Considering Steam Condensate Loss

et al. 2022

View full text Add to dashboard Cite

Steam is commonly used for heating in industrial production and transmitted through the steam network. Along with the decarbonization of energy generation and utilization, electric boiler and combined heat and power (CHP) became the alternatives for steam generation. Thus, the steam network is becoming tightly coupled with the power system, which brings new challenges in the simulation and operation of the combined electric and steam system (CESS). A novel simulation approach for CESS that can balance both the simulation accuracy and speed is proposed. Piecewise linearization of the saturated steam property is used for model simplification, which accelerates the simulation speed while ensuring the accuracy. Meanwhile, the heat loss of the steam network takes both condensate loss and heat dissipation into account, which further improves the accuracy of heat loss calculation. In view of model solving, two computation frameworks are provided for back-pressure and extraction condensing the CHP units, respectively. The accuracy and efficiency of the steam heating network model is verified through both the tree and ring steam network. In addition, a CESS case with both CHP and electric boilers is presented, and the results indicate such a system can reasonably improve the system stability and renewable energy consumption capability.

show abstract

Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method

Cited by 7 publications

References 21 publications

Optimization method of wind power consumption based on thermal storage tanks against the background of stepped carbon trading

Optimization method of wind power consumption based on thermal storage tanks against the background of stepped carbon trading

A Data Driven Security Correction Method for Power Systems with UPFC

A Novel Steady-State Simulation Approach for a Combined Electric and Steam System Considering Steam Condensate Loss

Contact Info

Product

Resources

About