Cross‐regional integrated energy system scheduling optimization model considering conditional value at risk

Yu, Xiaobao; Zheng, Dandan

doi:10.1002/er.5307

Cited by 17 publications

(10 citation statements)

References 22 publications

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“…In general, the actual wind speed characteristics follow the Rayleigh distribution, as described below 24 :

f_{WT} ((), v) = ((), λ / γ) {(v / γ)}^{λ - 1} \exp ([], - {(v / γ)}^{λ}),

where v represents the actual wind speed, γ , λ are the scale and shape factors. Therefore, the output of the WT is calculated as shown below 41 :

x_{italicWT, i} ((), v) = ({, \begin{array}{l} 0, v < v_{in}, v > v_{out} \\ \frac{v - v_{in}}{v_{rated} - v_{in}} P_{WT}^{rated}, v_{in} \leq v \leq v_{rated} \\ P_{WT}^{rated}, v_{rated} < v < v_{out} \end{array}),

where v rated , v in , v out are the rated speed, the minimum and maximum input wind speed.

P_{WT}^{rated}

represents the rated power.…”

Section: Problem Formulationmentioning

confidence: 99%

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

Yuan

Gao

et al. 2021

Int J Energy Res

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Summary As an important method for demand‐side management, the real‐time pricing can not only adjust the power balance between the supply and demand‐sides, but also effectively promote the internal energy dispatching behavior of the microgrid. In this article, a bilevel programming model is proposed based on the demand response to coordinate the master‐slave hierarchical relationship in the electricity market. At the upper level, the main grid determines the prices and generation for the supplier's maximum profit. At the lower level, we consider multiple microgrids, which operate in isolated‐island or grid‐connected mode, with photovoltaic generation system, energy storage system, and loads. Then, the optimal energy strategy is emerged for the user's maximum welfare according to the dynamic prices. Furthermore, we solve the upper and lower models separately to avoid user privacy being violated. The upper problem uses the genetic algorithm (GA), while the lower problem is handled by the branch and bound algorithm (BBA). A novel distributed algorithm named GA‐BBA is developed accordingly. Finally, taking an example of a smart grid system with three microgrids, the model and algorithm are simulated and compared. The results show that the peak‐to‐average ratio under the real‐time pricing is reduced from 1.6547 (fixed electricity price) and 1.4513 (time‐of‐use) to 1.2545. The peak‐to‐valley difference is also decreased by approximately 14.33%. In addition, the total social welfare of the system is increased by 145.53 kCNY (Chinese Yuan) through comparison with the existing model. The conclusions verify that the proposed bilevel programming model not only effectively reduces peak load but also increases the social welfare, which is more reasonable and realistic. Novelty Statement A novel bilevel optimization energy management system is proposed to optimize the hierarchical market structure between the main grid and multi‐microgrids (MMGs). A RTP mechanism based on demand response is formulated to balance the benefits between the main grid and MMGs. Moreover, the optimal energy strategy is obtained along with the dynamic price. Besides the operating cost, users' utility in microgrids are taken to our model. A hybrid distributed GA‐BBA is developed to protect the user's utility privacy.

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“…In general, the actual wind speed characteristics follow the Rayleigh distribution, as described below 24 :

f_{WT} ((), v) = ((), λ / γ) {(v / γ)}^{λ - 1} \exp ([], - {(v / γ)}^{λ}),

where v represents the actual wind speed, γ , λ are the scale and shape factors. Therefore, the output of the WT is calculated as shown below 41 :

x_{italicWT, i} ((), v) = ({, \begin{array}{l} 0, v < v_{in}, v > v_{out} \\ \frac{v - v_{in}}{v_{rated} - v_{in}} P_{WT}^{rated}, v_{in} \leq v \leq v_{rated} \\ P_{WT}^{rated}, v_{rated} < v < v_{out} \end{array}),

where v rated , v in , v out are the rated speed, the minimum and maximum input wind speed.

P_{WT}^{rated}

represents the rated power.…”

Section: Problem Formulationmentioning

confidence: 99%

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

Yuan

Gao

et al. 2021

Int J Energy Res

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show abstract

“…Operation strategy Cooling Heating Electricity Thermal characteristics [6][7][8][9][10][11] Q Q √ No consideration Open-loop [6][7][8][9][10][11] [12-16] Q Q √ Converted to Q Open-loop, 12,[14][15][16] rolling operation strategy 13 [17][18][19][20][21] Q Q √ Modeled by T (networks, [17][18][19] buildings, 18,20 or ambient 20,21 ), or by T and q (networks) [17][18][19] Robust MPC, 17 open-loop, 18,19,21 chance constraint SMPC 20 This work q,T q ,T √ Modeled by T and q (devices, networks, and ambient simultaneously)…”

Section: Scheduling Modelmentioning

confidence: 99%

“…Similarly, Liu established a model in the forms of heat constraints of heat‐supplied devices to study the collaborative planning of IES 7 . Still not considering detailed thermal processes, a bi‐level model was proposed by Lu et al 8 Considerable studies have been carried out based on such a modeling idea that oversimplifies the process variables 9‐11 . However, with the intersected energy topologies, diverse energy processes, and interdependent energy flows, 22 the IES is unquestionably of high complexity.…”

Section: Introductionmentioning

confidence: 99%

“…7 Still not considering detailed thermal processes, a bi-level model was proposed by Lu et al 8 Considerable studies have been carried out based on such a modeling idea that oversimplifies the process variables. [9][10][11] However, with the intersected energy topologies, diverse energy processes, and interdependent energy flows, 22 the IES is unquestionably of high complexity. Such scheduling models exclusively using the amount of heat transfer as decision variables result in certain shortcomings.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

Wei

Sun

et al. 2020

Int J Energy Res

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Summary A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

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“…Whale optimization algorithm (WOA) [17] and real-coded genetic algorithm with improved Mu¨hlenbein mutation (RCGAIMM) [18] have been employed on this issue considering the effect of both transmission losses and valve-point. In [19], scheduling optimization model of the combined energy system has been developed to meet the requirements of heating, cooling, and electrical loads. In [20], a quasidynamic pipeline model for EPHD with start-stop schedule of heat exchange stations is developed.…”

Section: Introductionmentioning

confidence: 99%

Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer

et al. 2020

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Cross‐regional integrated energy system scheduling optimization model considering conditional value at risk

Cited by 17 publications

References 22 publications

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

A bilevel programming approach for real‐time pricing strategy of smart grid considering multi‐microgrids connection

Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

Economic Power and Heat Dispatch in Cogeneration Energy Systems Using Manta Ray Foraging Optimizer

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