Scheduling optimization of wind power system based on cogeneration

Li, Zheng; Zhang, Rui; Zhang, Liping; Sun, Haiyan

doi:10.1002/2050-7038.12435

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Literature [16] proposed configuring a heat storage device on the side of the cogeneration unit to decouple the rigid constraints of "determining electricity by heat" and improve the grid-connection capability of wind power. Literature [17] used battery packs as energy storage devices to smooth wind power fluctuations while absorbing excess wind power.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Li¹,

Wang²,

Pang³

et al. 2023

Energy Engineering

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Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area. To improve the wind-power absorption capacity and operating economy of the system, the structure of the system is improved by adding a heat storage device and an electric boiler. First, aiming at the minimum operating cost of the system, the optimal scheduling model of the cogeneration system, including a heat storage device and electric boiler, is constructed. Second, according to the characteristics of the problem, a cultural gene algorithm program is compiled to simulate the calculation example. Finally, through the system improvement, the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme. The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation. The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem, which has a better optimization effect on the solution.

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Section: Literature Reviewmentioning

confidence: 99%

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Li¹,

Wang²,

Pang³

et al. 2023

Energy Engineering

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“…20 Apart from the common energy carriers such as thermal, cooling, or natural gas, hydrogen is recognized as a promising energy form 21 due to its high calorific and is gaining emphasis in recent years. 22 A multienergy management strategy is proposed, considering the complementarity of electricity, heat, and hydrogen. 23 With the techniques such as power to hydrogen (P2H) and fuel cell electric vehicles gradually commercialized, it is hopeful that P2H and hydrogen storage can become the alternatives to the expensive battery storage.…”

Section: Introductionmentioning

confidence: 99%

“…By reorganizing electricity, cooling, and heat of diverse areas, an integrated community energy system was established to guarantee sufficient scale to participate in wholesale markets 20 . Apart from the common energy carriers such as thermal, cooling, or natural gas, hydrogen is recognized as a promising energy form 21 due to its high calorific and is gaining emphasis in recent years 22 . A multienergy management strategy is proposed, considering the complementarity of electricity, heat, and hydrogen 23 .…”

Section: Introductionmentioning

confidence: 99%

A multi‐timescale schedule strategy for multi‐microgrids: A distributed approach

Zhang

2021

Int Trans Electr Energ Syst

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Multi-microgrids (MMGs) interconnect microgirds (MGs) with geographical adjacency and improve the overall efficiency, stability, and reliability of a regional energy system. The uncertainty brought by renewable energy resources (RESs) is one of the challenges facing the schedule of MMGs. To cope with it, a multi-timescale schedule strategy is proposed. Specifically, in day-ahead schedule, the on/off states of the units are determined. Then, based on the day-ahead plan and the more accurate estimation of RESs, the refined schedule is achieved in the intraday. And to enhance the flexibility of the MMGs system, multienergy complementarity is considered in this work. Moreover, how to achieve the coordinated schedule between MGs owned by different parties is another problem facing the MMGs system operation. To solve this, a distributed algorithm, namely predictor corrector proximal multiplier, is introduced. During the solution process, each MG only needs to reveal the exchange power to the virtual controller, which protects the privacy and reduces communication burden. Numerical simulation of an MMGs system

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“…This system effectively reduced the power deviation fluctuation with operating cost. In Reference 46, Li et al proposed a scheduling optimization of wind power based on cogeneration. The wind power generation system adds an electrolytic cell (EC) and a proton exchange membrane fuel cell (PEMFC) to absorb long‐term fluctuations, and the battery absorbs short‐term fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Application of chaotic quasi‐oppositional whale optimization algorithm on CHPED problem integrated with wind‐solar‐EVs

Paul

Roy

Mukherjee

2021

Int Trans Electr Energ Syst

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Summary In this article, a newly developed chaotic quasi‐oppositional whale optimization algorithm (CQOWOA) is employed to analyze the combined heat and power economic dispatch (CHPED) problem for the first time. In the suggested algorithm, chaotic quasi‐oppositional learning is imposed with a whale optimization algorithm (WOA) to enhance its convergence rate and reduce the generation cost. In this work, wind energy, solar energy, and electric vehicles (EVs) are scheduled with CHPED and developed in the proposed system for minimizing the expected generation cost. In the vehicle‐to‐grid system, EVs play a vital role, enabling to provide bidirectional power flow. The primary focus of the proposed CHPED scheduling is to optimize power generation cost by fulfilling various constraints. The presence of transmission losses and valve point effect of the thermal unit along with the uncertainty of wind, solar, and EVs introduce nonlinearity. Several optimization techniques have been studied for this proposed system to judge the effectiveness of the present optimization technique. The CQOWOA has been tested on 7‐unit, 24‐unit, and 48‐unit CHPED systems to validate its superiority. The study of the proposed system is extended further by incorporating two wind units. In addition, the proposed algorithm is tested on a large and complicated CHPED system scheduling with two wind units, one solar unit, and one EV unit. A comparison has been made based on a convergence profile and statistical results of the CQOWOA with WOA, gravitational search algorithm, to analyze the performance of the proposed algorithm. Moreover, to validate the robustness of the suggested CQOWOA approach, 30 IEEE CEC‐2020 benchmark functions are considered and the outcomes are compared with quasi‐oppositional WOA (QOWOA), chaotic WOA (CWOA), WOA, and two top performing algorithms, namely, hybrid levy particle swarm variable neighborhood search optimization and improved cellular univariate marginal distribution algorithm with normal‐Cauchy distribution.

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Scheduling optimization of wind power system based on cogeneration

Cited by 6 publications

References 20 publications

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm

A multi‐timescale schedule strategy for multi‐microgrids: A distributed approach

Application of chaotic quasi‐oppositional whale optimization algorithm on CHPED problem integrated with wind‐solar‐EVs

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