Information Gap Decision Theory Research Articles

Provision of the Flexible Ramping Product in a microgrid considering the trading strategies in the energy markets

The Flexible Ramping Product (FRP) market has been created as a new ancillary service market for the power systems to manage the severe ramp rates in the net load of the system. Microgrids (MGs) can provide the required ramp service for this market. The MG manager (MGM) sends its bids to the FRP market. The mutual effects of these bids on the MGM trading strategies in the day-ahead (DA) and real-time (RT) energy markets have not been considered in the previous studies. For this purpose, a new formulation is developed in this paper to model the MGM decisions in the FRP market while participating in the DA and RT energy markets. Moreover, the uncertainty on the probability of bid acceptance in the FRP market is modeled in the MGM decision-making problem using the information gap decision theory approach. The results show that changing the MGM strategies in participating in the markets, the objective function of the MGM improves when it participates in the DA energy, FRP and RT energy market, in comparison with the cases in which the MGM does not participate in the FRP market or the RT energy market. Also, the results show how the risk-averse MGM changes its strategies to provide the ramp service for the FRP market to make its decisions robust against the uncertain parameter.

Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program

Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme.

A two-stage IGDT-stochastic model for optimal scheduling of energy communities with intelligent parking lots

The proliferation of green mobility will bring multiple benefits to the society; however, it may be counterproductive for power systems if its integration is not properly planned. In this context, Intelligent Parking Lots have emerged as a valuable paradigm for integration of electric vehicles into energy systems. This framework consists of a set of vehicles that are managed as a whole and makes possible to exploit them as large storage facilities through their vehicle-to-grid capability. This particular feature may be significantly advantageous for energy communities since they can exploit parking lots as collective storage systems. In this paper, a two-stage optimal scheduling framework has been developed for optimal scheduling of energy communities. The proposal uses a stochastic representation of the state-of-charge of the lots with the end of accounting for random behaviour of uncertainties. On the other hand, the uncertainty of the upstream energy market is dealt with Information Gap Decision theory, resulting in an original hybridization that allows to adopt a risk-averse strategy by the operator. The optimization problem is formulated as a Mixed-Integer Linear programming model that can be efficiently solved by average solvers. A case study is performed to validate the new proposal and analyse the role of Intelligent Parking Lots in energy communities. The results evidence the advantages that electric vehicles may bring to communities if they are optimally exploited, highlighting their capability to enhance the efficiency and economy of the system.

A three-stage hybrid stochastic/IGDT framework for resilience-oriented distribution network planning

This article tackles the resilience-oriented distribution network planning (RODNP) problem utilizing a novel three-stage hybrid framework. The decision-making on the line hardening and DG placement is carried out in the first stage. In the second stage, emergency and normal operation optimization are conducted. The worst-case scenario is considered for evaluating the effect of a potential hurricane in post-event emergency operation conditions. In addition, a scenario-based stochastic approach is used to assess normal operation conditions. Finally, a risk-averse hardening re-plan is implemented in the third stage using the information gap decision theory (IGDT) notion as an effective non-deterministic optimization strategy under severe uncertainty. At this stage, distribution system planner (DSP) tries to develop a more resilient distribution network scheme against the most powerful hurricane possible while taking into account a predetermined parameter known as the resilience budget. The efficacy of the proposed approach is validated by conducting several case studies on the IEEE-33 bus test system. The results reveal that the proposed strategy may dramatically reduce the cost of both normal and emergency operations simultaneously.

Hybrid optimization for collaborative bidding strategy of renewable resources aggregator in day-ahead market considering competitors' strategies

Fueled by the increasing penetration of renewable energy in the power system, it is a promising way to form renewable energy aggregators so as to improve their competitiveness in the electricity market. This paper focuses on aggregators integrating wind, solar, hydropower and storage, where hydro works to moderate fluctuations and storage helps improve the profitability of the aggregator. The paper establishes a two-phase stochastic information-gap-decision-theory (IGDT) based bidding framework for such aggregator. The interaction between the aggregator and the competitive market is modeled as a stochastic bi-level problem, in which the strategic behaviors of self-interested competitors are fully considered. A density-based K-means clustering algorithm combining seasonal factors is proposed to achieve predicted offering scenarios of rival generators based on their historical market data. The hybrid stochastic-IGDT handles competitors’ offer strategies through scenario-based stochastic programming, and manages uncertainties of wind and solar generation under risk-averse and risk-seeking strategies. The results from Elia’s data demonstrate that the proposed bidding model not only shows better adaptability to the competitors' strategic behaviors, but also generates more profit for the aggregator through collaborative bidding of multiple sources.

Bi-level optimal scheduling of virtual energy station based on equal exergy replacement mechanism

Integrated Demand Response (IDR) is an effective way to make full use of the resources of the integrated energy system (IES) to achieve a positive interaction between supply and demand. Energy cascade utilization is one of the important technical principles of the IES to improve energy utilization efficiency and realize comprehensive benefit optimization. The design of an incentive mechanism is an important prerequisite for realizing IDR. When formulating the incentive mechanism, the coupling between the conversion efficiency of multiple energy sources and the depreciation of energy quality in the process of energy cascade utilization should be comprehensively considered. The exergy is the index that can evaluate the engineering value of any form of energy. Therefore, this paper unifies the multiple heterogeneous energy sources in the IES in the form of exergy through the energy quality coefficient. To more accurately reflect the difference in energy value of multiple heterogeneous energy sources during the time of invocation, an equal exergy replacement mechanism is designed according to the principle of high energy quality and high price. The regulated energy of the customer participating in the IDR is stored in the form of exergy, and the customer can exchange any form of energy at any time under the same exergy. On this basis, a bi-level optimal scheduling model for virtual energy station (VES) to participate in the day-ahead market is established, which realizes the improvement of multi-subject interests. And use Information Gap Decision Theory (IGDT) to deal with the risks posed by uncertainty in day-ahead market prices. Finally, the case study shows that the designed incentive mechanism can further reduce the energy purchase cost of customers, and VES can obtain higher benefits than only providing internal energy sales services. The IGDT method can reduce operational risk, which verifies the feasibility of the method.

An IGDT-based multi-criteria TSO-DSO coordination scheme for simultaneously clearing wholesale and retail electricity auctions

The electrical engineering world has encountered various issues due to integrating distributed energy resources in wholesale and retail electricity markets from various policy, modeling, and implementation perspectives. Hence, distribution system operators must be as more involved in operation of next-generation electricity markets as transmission system operators are. This paper proposes a new decentralized framework for clearing simultaneously both wholesale and retail electricity markets, focusing on various essential technical-economic analyses to explore major pros and cons in comparison with a centralized management strategy and two prevalent schemes. The proposed framework is based on optimality condition decomposition and is practically compatible with the liberalization rules of the EU’s Third Energy Package, so that achieves optimal solution of the theoretical models with a desirable solution precision in few clock time/iterations, without need for central coordinators or intermediary entities. Also, uncertainties of load demands and wind generation units are modeled by a new two-stage method based on information gap decision theory, in which the first stage obtains outcomes for next-hour market, and related deviations due to the mentioned uncertainties in real-time scenarios are explored in the second stage. The simulation results on 3-bus:15-node and IEEE 118-bus:33-node test systems confirm that the proposed method is the most applicable, mathematical feasible, successful in addressing severe uncertainties, and efficient in maximum utilization of distributed energy resources with thoroughly fair market prices; and in one sentence, this method is efficacious for implementation of short-term electricity markets and long-term strategic studies.

A hybrid probabilistic information gap decision theory based energy management of an active distribution network

The optimal operation of an active distribution network requires proper coordination between multiple entities like renewable energy sources, storage units, power electronic devices, demand response implementation, and network reconfiguration. In this context, this paper proposes optimal energy coordination and management scheme among various entities with different ownership in a reconfigurable active distribution network. The distribution system operator manages the coordinated energy procurement scheme from multiple sources to maximize the expected hourly profit of operation. Uncertainties of load, renewable generation, grid energy price, and the charging power of hybrid electric vehicles are modelled using a probabilistic “Hong’s 2m point estimate” approach. On the other hand, the uncertainty of the demand response is modelled using information gap decision theory. The optimization problem is solved in a hybrid framework encompassing probability and information gap decision theory. Simulation studies on a thirty-three-node distribution network validate the efficacy of the proposed method. With the proposed energy management and coordination scheme, the daily expected operating cost reduces by ∼6.66% while the daily expected energy loss reduces by ∼26.05%. Implementing a risk-averse strategy to hedge against the risk of uncertain demand response participation reduces the profit by ∼3.21% for a tolerance limit of 5%.

Information gap-based coordination scheme for active distribution network considering charging/discharging optimization for electric vehicles and demand response

The power fluctuation of load and distributed generation is a leading cause of load and voltage fluctuations and directly impacts the safe and efficient operation of the active distribution network in recent years due to the growing penetration of electric vehicles and distributed generation in the network. To optimize the load characteristics, a demand response model based on a logistic function is introduced, and the dynamic charging/discharging electricity price of electric vehicles based on the comprehensive load is considered in order to incentivize electric vehicle users to charge/discharge orderly. Then, a coordinated optimal model based on the information gap decision theory is constructed to cope with the uncertainty of distributed generation and ensure the operational economy and robustness of the active distribution network. Additionally, a population-based multi-objective state transition algorithm with a parallel search mechanism and communication mechanism was proposed to solve the demand response model, and second-order cone programming was utilized to solve the coordinated optimal model with complex constraints. Finally, the proposed comprehensive model and algorithm were investigated based on the IEEE 33 test system. The results show that the proposed model not only reduces the peak-to-valley difference in load by 24.38% but also significantly reduces the total operating cost by 13.41%. Simultaneously, the proposed algorithm increases the solving efficiency, verifying the feasibility and effectiveness of the optimal strategy and algorithm, which promotes the economical and reliable operation of the active distribution network.

Multi-objective operation of microgrids based on electrical and thermal flexibility metrics using the NNC and IGDT methods

This paper addressed the optimal operation of the microgrids (MGs) containing both the electrical and thermal loads. Combined Heat and Power (CHP) units, boilers, wind turbines, storage devices, Demand Response Resources (DRRs), as well as the power exchange possibility with the upstream wholesale market are the energy resources that have been considered as the portfolio of the decision maker. The aim of the MG operator is to provide the electrical and thermal loads of the network in the most economic and flexible way. Therefore, suitable metrics are developed in this paper in order to evaluate the flexibility of the system for both the electrical and thermal units. Then, considering both the flexibility and financial concerns, a multiobjective framework is proposed. The Information Gap Decision Theory (IGDT) method is employed to handle the uncertainties of the proposed problem. Since the objectives of the MG operator might be conflicting with each other, an efficient optimization approach should be employed to properly satisfy the operator’s policies. The Normalized Normal Constraint (NNC) approach is employed here as an efficient method that is able to find the evenly distributed Pareto solutions. The IEEE 33-bus test system is utilized to simulate the proposed problem and analyze the results. The performance of the proposed problem is approved using different scenarios. The simulation results justify the advantages and necessities of the proposed problem.

A robust optimization framework for scheduling of active distribution networks considering DER units and demand response program

The widespread penetration of new resources of electricity and thermal generation and storage in active distribution networks (ADNs) has affected many issues of the distribution network, such as generation scheduling. In consideration of that, in this paper, a problem of simultaneous scheduling of CHP units, electrical and thermal storage, auxiliary boilers, distributed generation system, and responsive loads is studied considering the uncertainties of the network. In this situation, the operator of the ADN performs day-ahead optimal scheduling of the electrical and heat generation units, taking into account the possibility of participation of responsive loads and exchanging electricity with the upstream market to minimize operating costs. Moreover, the uncertainty of energy market prices is also modeled using information-gap decision theory (IGDT) and robustness and opportuneness functions. To solve the problem, a mixed-integer linear programming is proposed. Furthermore, the proposed model is implemented on the IEEE standard 18-bus system, and its efficiency is evaluated by using various scenarios and sensitivity analyses of the optimization problem. • Scheduling of CHP units, storages, boilers, DGs, and responsive loads is studied. • The uncertainty of electricity market energy prices is modeled. • The proposed problem is modeled using a robust planning scheme. • To solve the problem, a mixed-integer linear programming is proposed.

Modeling the Microgrid Operator Participation in Day-Ahead Energy and Reserve Markets Considering Stochastic Decisions in the Real-Time Market

The penetration of the distributed energy resources in the distribution networks is facilitated by the structure of the microgrids (MGs). The MG operator (MGO) can schedule the MG resources to meet the local load and participate in the wholesale markets. In this article, a new model is developed for the MGO participation in the day-ahead (DA) (energy and reserve) and the real-time (RT) energy markets under uncertainties. For this purpose, the effect of the uncertainties of demand and generation from renewable energy sources on the MGO decisions is represented in a two-stage stochastic model. The MGO bids in the DA and RT markets are modeled as the first and the second stage decisions, respectively. Moreover, the information gap decision theory method is used to model the behavior of the MGO to address the uncertainties of the RT energy market price and the probability of calling the reserve. The results show that as the RT price uncertainty radius increases, the energy sold to the RT market decreases/increases in the risk-averse/risk-taker strategy. Furthermore, to manage the uncertainty related to the probability of calling the reserve, the reserve capacity provided by the MGO in the risk-averse and the risk-taker strategies decreases and increases, respectively.

Frequency‐constrained energy and reserve scheduling in wind incorporated low‐inertia power systems considering vanadium flow redox batteries

AbstractThis paper proposes a novel energy and reserve scheduling model for power systems with high penetration of wind turbines (WTs). The objective of the proposed model is to minimize the total operation cost of the system while static and dynamic security is guaranteed by preserving the frequency nadir, RoCoF, and quasi‐steady‐state frequency in the predefined range. Likewise, a supervisory, control, and data acquisition (SCADA) system is developed which allows Vanadium Redox Flow Batteries (VRFBs) to continuously communicate and participate in the primary frequency response. To cope with the uncertainties, adaptive information gap decision theory is used that ensures a target operating cost for the risk‐averse operator of the power system. The proposed scheduling model is applied on a modified IEEE 39 bus test system to verify the impacts of the fast reserve provided by the VRFBs in the dynamic frequency security enhancement of the power system with high penetration of WTs.

Flexible operation of integrated energy system with HVDC infeed considering multi-retrofitted combined heat and power units

For the coal-dominated power system, facilitating the thermo-electric decoupling of combined heat and power (CHP) units through flexibility retrofit technologies is an effective approach to strengthen the peak-shaving capacity of the system and promote wind accommodation. This paper focuses on exploiting the flexible regulation potential of the large-scale CHP units in the power and heat integrated energy system (PHIES) with high voltage direct current (HVDC) transmission infeed, in order to well deal with the dual peak-shaving pressure arising from the HVDC delivery power and the high share of fluctuant wind power. The internal and external retrofit technologies including zero output of low-pressure cylinders (ZO-LPC), bypass compensation heating (BCH), and thermal energy storage (TES) are adopted for CHP units as retrofit options. Firstly, the PHIES including multi-retrofitted CHP units, HVDC transmission systems, and a high share of wind power is described and modeled. Then, a two-stage day-ahead scheduling model based on the improved information gap decision theory (IGDT)-stochastic method is proposed considering the probability distribution information of uncertain variables. Finally, an annual system-wide benefits evaluation framework is formulated to assess the economic and environmental benefits of multi-retrofitted CHP units and compare the system gain value of different retrofit projects. Numerical results indicate that the thermo-electric feasible operating regions of CHP units can be comprehensively expanded with the cooperation of the proposed retrofit technologies, so as to greatly relieve wind curtailment and reserve risk of the whole system. Moreover, sensitivity analysis for diverse operation features of HVDC and different wind power installed capacity is illustrated to further verify the robust adaptability and effectiveness of multi-retrofitted CHP units in the PHIES with HVDC infeed.

A risk‐averse multiyear framework for resilience improvement planning of active distribution networks against hurricanes

This paper proposes a risk-averse strategy for resilience improvement multiyear planning for distribution networks against hurricanes, considering the uncertainty of load demand and wind power generation. For resilience enhancement, DG placement and line hardening are regarded as planning measures, and DG rescheduling, topology reconfiguration, microgrid forming, and priority-based load curtailment are considered as operational measures. The most conservative scenario is considered for modelling the high-impact/low-probability (HILP) event's impacts. Besides, the information-gap decision theory (IGDT) is utilized to acquire a robust solution protected against the different realization of uncertain variables. As the uncertain loads and wind power generation competitively tend to maximize their robust regions for a particular value of the budget of uncertainty, the ε-constraint method is applied to solve the proposed multi-objective IGDT-based resilience-oriented distribution network planning (RODNP) model. Afterwards, fuzzy decision-making is exploited to find the best solution among Pareto optimal solutions. The proposed risk-averse strategy is implemented on IEEE 33-bus test system, and various case studies are analyzed to demonstrate the proficiency of the approach. The results indicate that the proposed risk-averse strategy can optimally find the hardening schemes to achieve a resilient distribution network in a medium-term planning horizon with a yearly limited monetary budget.

A low-carbon peak-load regulation trading strategy for large-scale wind power integration using information gap decision theory

It is critical for power system to uphold operation security while considering the cooperation of thermal power and new flexible resources, such as energy storage and demand response (DR). This paper investigates the integration of carbon emission trading with peak-load regulation trading to analyze the effects of carbon change generated using thermal power, energy storage, and demand response, and builds a deterministic multisource low-carbon peak-load regulation trading optimization model. To describe wind power uncertainty, we apply information gap decision theory (IGDT) to quantify the information gap between the predicted and actual data of wind power; subsequently, we construct a multisource low-carbon peak-load regulation trading optimization model based on IGDT considering the uncertainty. Finally, a local power grid in Northwest China is considered as a case study, and we establish regular, low-carbon, stochastic and comprehensive four peak-load regulation scenarios to analyze the impact of carbon emissions trading, energy storage, and DR involved in wind power peak-load regulation trading. The results show the following. (1) The individual application of the energy storage system (ESS) and DR can reduce peak-load regulation cost by 4.64% and 7.68%, respectively, whereas a 8.38% reduction is achieved when they are applied together. (2) When considering the uncertainty of wind power, the grid connected power of wind power is reduced by 2.15% and the total peak shaving cost is increased by 10%. With the increase in uncertainty, the cost of peak shaving trading scheme is always lower than the expected value, indicating that the IGDT method is effective. (3) When the price of the carbon emission trading ranges from 85–95 CN¥/t, the peak-load regulation cost of thermal power units are compensated; moreover, the peak-load regulation cost of wind power and allowable uncertainty reach optimum values when the acceptable price is less than 80 CN¥/t in the worst scenario. In summary, the ESS and DR can enhance the flexibility of the system for peak-load regulation, thus increasing wind power grid connection, and carbon emissions trading can improve the net economy of peak-load regulation of thermal power units. The IGDT method can describe the uncertainty risk of wind power output and establish a peak-load regulation trading scheme that satisfies the expectations of decision makers with different risk attitudes. The peak-load regulation trading scheme must carefully consider the carbon emission trading price: if the carbon emission trading price is excessively high, it will result in a retaliatory grid connection of wind power to compensate for the carbon trading costs, and the system cost will increase rapidly.

A three-level framework for strategic participation of aggregated electric vehicle-owning households in local electricity and thermal energy markets

The impact of electric vehicles (EV) charging strategy will not be limited to power systems as integrated electricity, natural gas and thermal energy systems have become increasingly interconnected. We introduce a three-level framework for the aggregated electric vehicle-owning households (AEVH) to strategically participate in local electricity and thermal energy markets as a price-maker, while considering the strategic behavior of the integrated energy service provider (IESP) in the wholesale electricity market (WEM) also as a price-maker. The AEVH operator forms the first level, while IESP and WEM operators are integrated at the second and third levels, respectively. To solve the three-level problem, the second and third levels are modified as a single-level problem through the Karush–Kuhn–Tucker (KKT) conditions, then the equilibrium point of the resulting single-level problem and the first level is achieved through two-step iterative method. At the first level, the arrival/departure time and daily traveled miles of EV fleets are modeled via stochastic scenarios, while renewable energy production at the second level is dealt with by information gap decision theory (IGDT). Ultimately, different case studies verify that AEVHs can deploy their thermal flexibility together with the smart charging strategy of the EVs to influence the local electricity, thermal energy and even WEM prices. Using the proposed three-level optimization framework reaches the best point of equilibrium between different market players. The outcomes prove the effectiveness of the proposed model. Based on the results, the AEVH can deploy the proposed model to diminish the WEM price by 2.1%, while the local electricity price was dropped by 18.85%. Furthermore, the thermal energy price was reduced by 5.82%, which illustrates that EVs can influence the thermal energy market through the combined heat and power units.

Operation of energy hubs with storage systems, solar, wind and biomass units connected to demand response aggregators

• Day-ahead scheduling has been carried out for energy hub. • Uncertainties of demands, PV, solar heat, wind and electricity prices are considered. • Demand response aggregator has been used as an input source of electricity. • Demand response effect on EH operation cost has been investigated. • Effect of storage systems on hub operation costs have been evaluated. Energy Hubs (EHs) play an important role in sustainable cities; they are multi-carrier energy systems that can satisfy different energy needs of consumers by relying on the conversion and storage of energy sources as well as renewable energy sources. With efficient and reliable energy supply, EHs may significantly contribute in developments of sustainable cities. In this paper, day-ahead scheduling of EHs is done, while they are connected to demand response aggregators. The studied EH includes photovoltaic and wind renewable sources, biomass, hydrogen electrolyzer, combined heat and power unit, solar heater, boiler, electric, thermal and hydrogen storage systems. Besides electric grid and gas network as input sources, EH may purchase electricity from demand response aggregators. Information gap decision theory (IGDT) is employed as a risk-aware method to handle uncertainties of electric, thermal and hydrogen demands, photovoltaic and wind power, solar heat and electricity prices. The scheduling is carried out from the perspective of the uncertainty free, risk-averse and risk- seeking decision-makers. The problem is formulated as a mixed-integer model and is solved using CPLEX solver in General algebraic modeling system (GAMS). The impact of risk awareness and deviation factors of critical and target costs on day-ahead scheduling and EH operation costs is investigated. The results show that the transaction with demand response aggregator decreases EH operation cost by 20.1%. The results also show that electric, thermal and hydron storage systems respectively decrease the operation cost by 3, 1.7 and 2.1%.

Optimal Scheduling of Reconfigurable Microgrids in Both Grid-Connected and Isolated Modes Considering the Uncertainty of DERs

In this study, an operation strategy is introduced for distributed energy resources (DERs) in reconfigurable microgrids (MGs) to improve voltage profiles, minimize power losses, and boost the system performance. The proposed methodology aims to minimize power loss and energy not supplied (ENS) in MGs with an intelligent share of DERs and network reconfiguration in grid-connected and islanded modes. Due to the inherent uncertain nature of renewable DERs, these sources’ power output is considered as an uncertain parameter, and its effect on the system characteristics is analyzed. The state-of-the-art information gap decision theory (IGDT) approach is utilized to explore different decision-making strategies in the energy scheduling of reconfigurable MGs to deal with such uncertainty. To validate the effectiveness of the proposed method, the IEEE 33-bus radial system is utilized as the test MG. The simulation results show the importance of energy storage systems and reconfiguration in dealing with uncertainty and improving system reliability.

Hybrid Game Optimization of Microgrid Cluster (MC) Based on Service Provider (SP) and Tiered Carbon Price

Carbon trading is a market-based mechanism towards low-carbon electric power systems. A hy-brid game optimization model is established for deriving the optimal trading price between mi-crogrids (MGs) as well as providing the optimal pricing scheme for trading between the microgrid cluster(MC) and the upper-layer service provider (SP). At first, we propose a robust optimization model of microgrid clusters from the perspective of risk aversion, in which the uncertainty of wind and photovoltaic (PV) output is modeled with resort to the information gap decision theo-ry(IGDT). Finally, based on the Nash bargaining theory, the electric power transaction payment model between MGs is established, and the alternating direction multiplier method (ADMM) is used to solve it, thus effectively protecting the privacy of each subject. It shows that the proposed strategy is able to quantify the uncertainty of wind and PV factors on dispatching operations. At the same time, carbon emission could be effectively reduced by following the tiered carbon price scheme.