An incentive mechanism design using CCHP-based microgrids for wind power accommodation considering contribution rate

https://doi.org/10.1016/j.epsr.2020.106434Get rights and content

Highlights

  • A comprehensive wind power accommodation trading model including CCHP-based MGs is established. scenario-based stochastic programming is applied to depict the uncertainty of the CCHP-based MGs.

  • An index termed as contribution rate is proposed to evaluate the contributions of the participant's and a benefit allocation mechanism is designed using asymmetric Nash bargaining theory.

  • A distributed optimization method using the ADMM is proposed to implement the incentive mechanism.

Abstract

Due to the rapid growth and the volatility of wind power, wind power accommodation becomes much more challenging. As combined cooling, heating and power (CCHP)-based microgrids (MGs) operators(CMGOs) can schedule the local multi-energy (cooling, heating and power) generation, storage and implement integrated demand response to accommodate abandoned wind power. In this paper, an incentive mechanism is proposed to facilitate wind power accommodation trading.In themechanism, a wind power supplier (WPS) organizes many CMGOs to participate in the wind power accommodation trading. Besides, a novel index termed as contribution rate, is proposed to evaluate the participants’ contributions to the wind power accommodation trading. Then, based on participants'contribution rate the benefits allocation mechanism is designed. As different CMGOs tend to make decisions independently, it is impractical to solve the optimization problem in a centralized method. A distributed method is proposed to solve the optimization problem using the alternating direction method of multipliers decomposition technique. The simulation results demonstrate that the abandoned wind power is accommodated by CMGOs well, and both WPS and CMGOs get benefits from the wind power accommodation trading according to their contribution rates.

Introduction

Power systems are facing a high penetration of wind generation recently. The total installed wind power capacity reached 597 GW at the end of 2018 [1]. However, wind power accommodation is not satisfactory due to the volatility of wind power. Take China as an example, the amount of abandoned wind power is 27.7 billion kWh in 2018 [2]. Therefore, it is urgent to solve the problem of wind power accommodation.

A number of studies including [3], [4], [5] have focused on the wind power accommodation.In [3],an economic dispatching model for accommodating wind power is built using electric water heaters. In [4], to achieve optimal accommodation of wind power, an improved node marginal price model is proposed. In [5],to achieve maximum profit of wind power producers, a stochastic bi-level programming model with wind power producerforelectric demand response and electric vehicle aggregators is established and the bi-level problem is converted to the equivalent single-level through the KKT conditions.

Recently, combined cooling, heating and power (CCHP)-based microgrid (MG) becomes more popular due to its high fuel efficiency and environmentally friendly [6]. Many studies are devoted to establishing dispatching models for individual CCHP-based MGs considering wind power accommodation which include [7], [8], [9], [10], [11], [12], [13].In [10], an economic dispatch model for wind power integrated system is built considering the dispatchability of power to gas. In [11], a day-ahead scheduling model for integrated electricity and district heating system considering the thermal inertia of buildings is proposed. In [12], a new integrated heat and power dispatch model that considers the thermal inertia of the district heating network is formulated to improve the flexibility of CHP units for wind power integration. In [13],a CHP dispatch model considering the participation of advanced adiabatic compressed air energy storage for integrated electricity and heating systems and accommodating wind power. However, as different CMGOs and the wind power supplier (WPS) may belong to different economic entities, an incentive mechanism is needed to motivate CMGOs participate in thewind power accommodation trading.

To achieve the optimal schedule of multiple MGs, some game theoretical strategies are proposed in [14], [15], [16]. Reference [17] proposes a cooperative power dispatching method to achieve the optimal operation of multiple MGs and the uncertainties of demand are considered in [18]. However, in [17] and [18], the multiple MGs are dispatched by one operator, this may not always be the case. Besides, as the CMGOs tend to protect their privacy, it is difficult to collect the information needed for centralized optimization. To overcome these drawbacks, reference[19] proposes a distributed energy scheduling method in MGs and reference [20] proposes a distributed convex optimization framework to enable energy trading between islanded MGs.As the good convergence ofalternating direction method of multipliers (ADMM) [21], references [22] and [23] adopt ADMM to design distributed algorithms to achieve distributed optimization of optimal power flow.

There are some price-based electricity trading mechanisms, which include [24] and [25]. In [24], a decentralized price-based trading algorithm is proposed for load aggregators and generators. In [25], based on noncooperative game theory, a price mechanism is designed to facilitate energy trading among MGs. however, these price-based mechanisms require frequent bidding, which could increase market transaction costs.Nash bargaining theory is adopted in recent researches to designmarket mechanisms, which can avoid frequent bidding. In [26], an incentive mechanism based on symmetric Nash bargaining (SNB) theory are designed to motivate MGs to participate in energy trading. In [27], an incentive mechanism using SNB theory is designed to motivate MGs to provide ramping capacity. In [28],an incentive mechanism using SNB is designed to motivate distributed energy resource owners to generate reactive powerfor local voltage control. In [29], a mechanism design based on asymmetric Nash bargaining(ANB) is designed to motivate distributed energy resource in energy and capacity markets. In [26], [27], [28], the SNB theory is widely adopted to equallyallocate the benefits among market participants and cannot identify different contributions belonged to market participants. However, a well-designed incentive mechanism shouldidentify the different contributions belonged to market participants and accordingly allocate the benefits.

Therefore, to identify participants’ contributions to the wind power accommodation trading, a novel index termed as contribution rate is proposed. Based on the participants'contribution rates, an incentive mechanism which can identify participants’ contributions and accordingly allocate the benefits is proposed in this paper. The main contributions of this paper are summarized as follows:

  • 1)

    Acomprehensive wind power accommodation trading model including CCHP-based MGs is established. In the model, multi-energy generation, storage and integrated demand response are considered. Furthermore, the uncertainty of CCHP-based MGs is considered.

  • 2)

    To achieve fair benefits allocation, an index termed as contribution rate is proposed and a benefit allocation mechanism is designed using ANB theory.

  • 3)

    To protect different CMGOs’ privacy, a distributed optimization method using the ADMM is proposed to implement the incentive mechanism.

The rest of the paper is organized as follows. Components model of the CCHP-based MGs are established in Section 2. In Section 3, the operation optimization problem of one CMGO is formed. Further, we form the optimization problem of wind power accommodation trading with N CMGOs. In Section 4, the distributed implementation of the incentive mechanism design is explained in detail. In addition, the benefit allocation mechanism is designed. Simulation results and discussion are presented in Section 5. Finally, we conclude this paper in Section 6.

Section snippets

Components modelling

As shown in Fig. 1, a WPS organizes a number of CMGOs to participate in the wind power accommodation trading. Through the exchange of information between the WPS and CMGOs, the CMGOs cooperate to accommodate abandoned wind power for the WPS.

Due to the uncertainty of wind power, the optimal operation of CCHP-based MGs is a stochastic optimization problem [30,31]. In this paper, scenario-based stochastic programming is used to depict the uncertainty of wind power. First, the basic scenarios are

The formation of optimization problem

To accommodate the power of distributed energy resources, some researches have already proposed using MG clusters to accommodate the power of distributed energy resources [35]. With the popularity of CCHP-based MGs, it becomes a new way to accommodate abandoned wind power.

Distributed implementation and the benefit allocation mechanism

In this paper, an incentive mechanism for CCHP-based MGs in accommodating abandoned wind power is proposed. The incentive mechanism consists of two parts. First, a distributed solution is proposed to solve the optimization problem (3.2) which involves N CMGOs in cooperating to accommodate abandoned wind power. Secondly, based on the solutions to the optimization problem (3.2), a novel index termed as contribution rate is proposed and ANB is adopted to design benefit allocation mechanism.

Case studies

In this paper, we consider a WPS with 5 CMGOs and 10 CMGOs to participate in the wind power accommodation trading, respectively. Besides, to verify the effectiveness of the proposedapproach, we compare ANB method with widely used SNB method. Note that, in traditional SNB model, all CMGOs have identicalweights without identifying different CMGOs’ contributions,δiCMGO=(1δWPS)/Nwhere N is the number of CMGOs. In this paper δWPS=0.2 for ANB and SNB, Besides, ANB and SNB methods have identical

Conclusion

In this paper, the wind power accommodation trading among CCHP-based MGs is studied based on ANB.An incentive mechanism is designed to motivate CMGOs to participate in the wind power accommodation trading. Case studies validate the effectiveness of the wind power accommodation mechanism. Compared with the case without wind power accommodation trading, the benefits of CMGOs and WPS are positive which makes them willing to participate in the wind power accommodation trading. Additionally, in

CRediT author statement

Jiqun Guo: Conceptualization, Methodology, Writing-Original draft, Writing-Reviewing and Editing, Visualization.

Yang Li: Resources, Supervision, Funding Acquisition, Project Administration.

Yunwei Shen: Software, Formal Analysis, Data Curation, Writing- Reviewing andEditing.

Hefeng Li: Validation, Formal Analysis, Investigation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personalrelationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China (Grant No. 51777030). This work was also supported by the Key Program “Secure and Efficient Operation Mechanism for Water, Electricity and Integrated Energy Systems (IES)” supported by Smart Grid Collaborative Fund (Grant No. U1966204).

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