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• This paper proposes a bi-level Stackelberg low-carbon economic dispatch model with reward and penalty stepped carbon trading mechanism, horizontal and vertical integrated demand response, and dynamic supply and demand multi-energy pricing. • This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the stepped carbon trading mechanism on the operation of the CIES. • An innovative analysis of the impact of single and two-factor carbon interval length, carbon growth rate, and carbon base price on the CIES is presented. • The sensitivity analysis of horizontal integrated demand response is also carried out. • Through horizontal and vertical integrated demand response, the dynamic supply and demand multi-energy pricing of CIES is further improved. To solve the problem of coupled and coordinated dispatch of multi-energy between campus integrated energy system operator and users. A bi-level Stackelberg low-carbon economic dispatch model with horizontal and vertical integrated demand response and dynamic supply and demand multi-energy pricing (DSDMEP) is proposed. Firstly, the DSDMEP is established to effectively coordinate the interests of the upper CIESO and lower users. Secondly, the combined heat and power containing the power to gas and carbon capture system and the reward and penalty stepped carbon trading mechanism (SCTM) are introduced on the energy supply side to minimize carbon emissions. Then, the HVIDR is introduced to the users to increase the energy coupling and adjustable price characteristics of supply and demand. Finally, the results show that the proposed model compared to other scenarios has its carbon emissions reduced by 21.72%, the benefits of CIESO are increased by 27.86%, and the cost of the users is reduced by 6.9%. This paper also provides a multi-angle sensitivity analysis of the impact of different parameters of the SCTM on the operation of the campus integrated energy system (CIES), as well as a sensitivity analysis of the HVIDR and peak and valley electricity price difference. [ABSTRACT FROM AUTHOR]

• Constructed an operation architecture of the coupling of energy supply and demand. • Established the comprehensive demand response mechanism based on time-sharing energy prices. • Established the minimum carbon emission cost economic scheduling model as an objective function. • The scheduling method could achieve the goal of low-carbon economic dispatch. Aiming at the problems of low energy utilization and environmental pollution in China's livestock and poultry breeding industry under the carbon peaking and carbon neutralization goals, this paper proposes an optimal scheduling model for multi-energy coupling integrated energy system with ladder-type carbon trading mechanism and demand response. First, based on the construction of a reward and punishment model with ladder-type carbon trading mechanism, this paper explores the potential interaction between the coupling ability of multiple heterogeneous energy sources and carbon emissions in consideration of the impact of carbon capture equipment on carbon emissions. Second, in view of the diversity and complexity of demands for agricultural energy supply, the energy supply side equipment model and the comprehensive demand response model of electricity, heat, and gas are established, and a low-carbon economic scheduling model of integrated agricultural energy system is proposed, aiming at minimizing the total operation cost of the system. Finally, the model is solved by CPLEX, and the coupling system of a 6-node power distribution network, a 6-node gas network, and a 4-node heat supply network is used to verify the effectiveness of the proposed model and method and optimize the operation of the integrated agricultural energy system. [ABSTRACT FROM AUTHOR]

• Taking carbon in RIES as energy flow, which is equivalent to electricity-heat-gas to ensure the carbon quota requirements of the system. • Proposing a new demand-side response mechanism to meet the purpose of reducing carbon emissions. • Scheduling RIES with comprehensive consideration of economy and low carbon. • Proposing a new RIES ladder carbon price trading mechanism to match the purpose of demand side management. Effective mitigation and reduction of carbon emissions is consistently underscored in Regional Integrated Energy Systems (RIES), aligning with the goals of carbon-neutralization. However, the inherent trade-off between carbon emissions reduction and economic operation is an obvious challenge not only in power systems but especially in the coupled electricity-gas-heat energy systems. This paper develops a bi-level low-carbon optimal scheduling model, highlighting reliable operation and the balance of supply and demand of RIESs with demand-response uncertainty. In this paper we firstly redefined the carbon emission of a RIES as a carbon flow and proposed a reward and punishment carbon trading mechanism based on the ladder carbon price. Secondly, we designed an incentive-based demand-side response policy within the subsystem encompassing electricity, gas, and heat. Concurrently, the probability density distribution functions of transferable, replaceable, and interruptible loads are computed separately. Based on the above, the deviation of the load reduction and the actual load is recalculated to delimit the constraint of the demand-side response. Finally, the mechanism of carbon trading and the demand-response is leveraged to address the economic scheduling incorporating operation cost, carbon transaction cost, imbalance costs of supply and demand, and load loss. Simulations demonstrated in the paper prove that the optimal solution with demand-response uncertain may effectively increase the economic performance and reduce carbon emissions of a RIES. [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

• A dynamic equivalent modelling approach for the representation and analysis of the dynamic performance of distribution networks with high penetration of Electric Vehicles (EVs) is developed. • The influence of EV charger control parameters on the resulting dynamic responses and modelling needs is investigated. • The impact of EV penetration level on the dynamic behavior of distribution networks is analysed. • The variability of model parameters under different EV penetration levels is presented. During the last few years, there is a worldwide drive for the electrification of the transportation sector to achieve reduced carbon emissions. In this context, the global fleet of electric vehicles (EVs) is steadily increasing. Consequently, in the near future, EVs will become a significant part of the overall power demand. Therefore, the distinct characteristics of the power converters, used in EV charging stations, will have a clear impact on power system dynamics. Therefore, new methods and models are required to simulate and analyse the dynamic performance of distribution networks (DNs) hosting EVs as well as to represent their aggregated dynamic behaviour for transmission level dynamic studies. Towards this objective, in this paper, an equivalent model, based on variable order transfer functions, is proposed to analyse the dynamic properties of EVs as well as to simulate the dynamic behavior of EV-rich DNs. Initially, a sensitivity analysis is performed to quantify the influence of the different control settings of the EV chargers on the resulting dynamics. The analysis reveals that control settings affect considerably the underlying dynamics. Nevertheless, in all cases the proposed equivalent model provides very accurate estimates. Moreover, the impact of EV penetration level on DN dynamics is quantified using a set of metrics. Additionally, a parametric analysis is conducted to verify the applicability of the proposed equivalent model for the dynamic analysis of DNs under specific EV penetration levels. Finally, guidelines for the derivation of generic parameters for the developed equivalent model are proposed. [ABSTRACT FROM AUTHOR]

• A method for new power electronic equipment is proposed. • Novel software for the developed hardware is proposed. • The device decentralises the aircraft's power system. • This solution increases overall efficiency and safety. The More Electric Aircraft concept has arisen in an attempt to achieve the goals stated by different international organisations in terms of the concept of future aircraft. While some of these requirements are related to reducing the negative effects of airfreight such as CO2 emissions or fuel consumption, other requirements focus on aircraft safety enhancement. Nevertheless, if the way to achieve the vast majority of these goals is based on increasing electrification level of aircrafts, then their electric power system must be reviewed and reconsidered. Currently, literature proposes device redundancy as the main option for meeting aircraft safety requirements. This paper proposes alternative architecture, which consists of merging a new source code and recently developed topology, which makes it possible to decentralise the aircraft's DC bus, in order to increase its safety and meet the stated goals. While the new topology has been validated through small-scale prototyping, proposed software strategy has been validated in a virtual environment using simulations. The architecture developed enables a path to be selected in the electric power system: examining different options in the system, analysing which of those options are the most efficient and, finally, sending signals to configure the power system connections to power the load. Based on the results of those tests, the alternative proposed satisfactorily powers selected loads. [ABSTRACT FROM AUTHOR]

In this paper, we aim to analyse the impact of hydrogen production decarbonisation and electrification scenarios on the infrastructure development, generation mix, CO 2 emissions, and system costs of the European power system, considering the retrofit of the natural gas infrastructure. We define a reference scenario for the European power system in 2050 and use scenario variants to obtain additional insights by breaking down the effects of different assumptions. The scenarios were analysed using the European electricity market model COMPETES, including a proposed formulation to consider retrofitting existing natural gas networks to transport hydrogen instead of methane. According to the results, 60% of the EU's hydrogen demand is electrified, and approximately 30% of the total electricity demand will be to cover that hydrogen demand. The primary source of this electricity would be non-polluting technologies. Moreover, hydrogen flexibility significantly increases variable renewable energy investment and production, and reduces CO 2 emissions. In contrast, relying on only electricity transmission increases costs and CO 2 emissions, emphasising the importance of investing in an H 2 network through retrofitting or new pipelines. In conclusion, this paper shows that electrifying hydrogen is necessary and cost-effective to achieve the EU's objective of reducing long-term emissions. • A new formulation is proposed for natural gas network retrofitting to hydrogen (H 2). • 30% of EU electricity in 2050 will supply 60% of the H 2 needs. • Flexible electricity-based H 2 production boosts renewables by 48%. • Using electricity for H 2 production cuts CO 2 emissions by 35%. • 11% gas network retrofit covers H 2 demand with no new pipelines required. [ABSTRACT FROM AUTHOR]

• The proposed joint demand response and dynamic pricing strategies can make the net grid demand curve smoother and have practical significance. • The dynamic pricing strategy helps to realize the joint dispatch of multiple demand response resources in the region and alleviate the grid pressure during peak hours. • A two-layer optimization model is proposed to not only realize the economy of joint operation of multiple energy stations, but also make the net grid demand curve smoother through joint demand response. In recent years, countries around the world have made great efforts to coordinate energy green and low-carbon development and energy supply. In this context, it is of great significance to build energy stations that can greatly absorb renewable energy. The coordinated operation of multi-energy stations in the region can expand the role of energy stations and strengthen energy complementarity between regions. Aiming at the problem of energy interaction and coordinated operation of multi-energy stations in regional integrated energy system, this paper proposes a two-layer scheduling optimization model, which can effectively reduce the system operation cost and carbon emissions. At the same time, considering the joint demand response, it can effectively reduce the peak power supply pressure of the power grid. In addition, in order to ensure the accuracy and efficiency of the solution, this paper improves the traditional NSGA-II algorithm, greatly improving the overall performance of the algorithm. Finally, the simulation analysis is carried out through an example. The results show that the operation optimization model proposed in this paper can effectively reduce the total energy cost of the system, reduce carbon dioxide emissions, and reduce the peak pressure of the power grid. The coordinated operation optimization method is effective and feasible. [ABSTRACT FROM AUTHOR]

• Proposed cooperative game model of hierarchical trans -regional electricity trading. • Analyzed the impact of carbon quota systems on trans -regional electricity trading. • Proposed a reasonable profit distribution scheme based on the Owen graph value. • Demonstrated that the formation of priori union can improve the profits of trans-regional electricity trading. • Under loose low-carbon demand policy, carbon quota sharing responsibility are more effective than producer responsibility. Trans-regional electricity trading is a meaningful way to optimize resource allocation and achieve carbon peak and carbon neutrality goals. In this paper, we construct the trans -regional electricity trading cooperative game models considering the impact of local microgrid integration. Based on the cooperative game theory with coalition and graph structures, the optimal mode of trans -regional electricity cooperation and the impact of different carbon quota systems on cooperation profits are studied. The profits obtained through the trans -regional electricity trading are reasonably distributed by the profit distribution scheme based on the Owen graph value. The theoretical and numerical simulation results demonstrate that the local microgrid and the bulk power grid form a priori union, and then participate in the trans -regional electricity trading can obtain more profits. In addition, when the low-carbon policy on the demand side of electricity becomes more relaxed, the implementation of a carbon quota shared responsibility between producers and consumers will be more effective than producer responsibility. Compared with the Myerson value, the profit distribution scheme based on the Owen graph value is more suitable for distributing the profits obtained through trans -regional electricity trading. [ABSTRACT FROM AUTHOR]

• Forecasting-test framework for long-term power system planning. • Coal-fired generators can be completely withdrawn from the power system by 2060. • China's power capacity mix will transition to wind and solar generators dominated. • Biomass and gas-fired generators will take on the role of reserved generators. • The timing of achieving China's zero-coal power system may be advanced or delayed. Facing the challenges of climate deterioration, environmental pollution, and energy shortages, China has put forward the goals of "carbon dioxide peaking and carbon neutrality", and the electricity sector will be the key area and the main direction of carbon emissions reduction. To prospect China's power system and explore the potential for establishing China's zero-coal power system in the future, this paper proposes a forecasting-test framework for long-term power system planning. We predict, evaluate, and analyze China's power system from 2025 to 2060 using the proposed forecasting-test framework. During this process, the power capacity mix and the electricity generation mix of China's power system will gradually transition from coal-fired generators dominated to wind and solar generators dominated. Moreover, the roles of different generators and energy storages are illustrated through the seasonal comparison and sensitivity analysis of the installed capacity of generators. The results indicate that coal-fired generators can be completely withdrawn from the power system, confirming the feasibility and possibility of achieving China's zero-coal power system by 2060. Although the timing may be advanced or delayed in different scenarios, achieving China's zero-coal power system will require exercising nationwide planning and making joint efforts of various sectors from now on. [ABSTRACT FROM AUTHOR]

• DR prioritizes reducing peak demand and spillages over participating in the reserves market. • DR can reduce up to 12% of total system costs, mainly from peaking plant investment needs. • Although DHW offer more flexibility, H&C systems are more relevant for reserves. • DR is prepared to provide more than 50% of total reserve needs. • Overestimating DR needs, increases system emissions. Balancing markets will become more and more relevant with the increased volatility in the electricity system due to the increase in the renewable quota. New policies are paving the way for customers flexibility participation as demand response in reserve products. This paper contributes with an assessment of the impact of demand response participation in the reserve market when planning the electricity system's operation and investment in new technologies. The model used has been conveniently upgraded and a set of scenarios have been raised to conduct the analysis. The residential and services sectors' consumption for heating, cooling, hot water, and electric vehicles are considered as sources of flexibility. Each one has their own modeling to represent their nature. Main findings show that demand response receives and offers more benefits for the system on the wholesale market than in balancing services, although their participation in them is quite relevant. This is due to the decrease in firm capacity investment needs thanks to reducing systems' peak technologies and the decrease of spillages from renewables. Additionally, increasing demand response percentages in the systems lead to cost reduction. However, there is a limitation associated with an increase of CO2 emissions due to the usage of existing polluting technologies to avoid investments in storages. Finally, flexibility providers are compared to determine their flexible capabilities. [ABSTRACT FROM AUTHOR]

• A two-stage coordinated planning framework is proposed. • A siting optimization submodel for energy station and supply network is proposed. • A sizing optimization submodel for energy devices and pipelines is proposed. Coordinated siting and sizing for energy stations and supply networks in low-carbon park-level integrated energy systems are significant for economic improvement and carbon emissions reduction. To minimize total planning costs and carbon emissions of park-level integrated energy systems, a two-stage coordinated siting and sizing framework for the energy station and supply network of a low-carbon park-level integrated energy system considering generalized energy storage is proposed in this paper. In the first stage, a siting optimization submodel for the energy station and supply network in park-level integrated energy system is established to minimize the total comprehensive energy distance values from the energy station to all load stations, in which the virtual network flow theory is introduced to characterize and linearize the connectivity constraints of the supply network. Then, a sizing optimization submodel for the energy station and supply network in park-level integrated energy system is proposed in the second stage based on the optimal siting strategies of park-level integrated energy system, in which the generalized energy storage is considered to minimize planning costs and carbon emissions. Finally, case studies are conducted on an improved 55-bus park-level integrated energy system to verify the effectiveness of the proposed model, and simulation results demonstrate that the proposed model outperforms other existing planning models in terms of achieving lower total comprehensive energy distance values, total planning costs and carbon emissions. [ABSTRACT FROM AUTHOR]

• A joint energy-frequency regulation power market is proposed for energy transition. • Environmental cost of fossil-fueled generation is incorporated into electricity trading. • Renewable energy generation is motivated to actively manage uncertainty and intermittency of outputs. • Extensive numerical simulations are conducted to validate the proposed system. Striving to develop renewable energy generation (REG) has become a global trend. Due to high carbon emissions, fossil-fueled generation units (FFGUs) in an electrical energy market such as the day-ahead and real-time electricity markets are facing unprecedented constraints. From the perspective of FFGUs, an effective way is to change their market roles from energy suppliers with high environmental costs to secure and reliable frequency regulation service providers. To help FFGUs successfully complete the transition, a bi-level optimization model for the joint energy and regulation service market is proposed in this paper. Different from existing joint markets, both the environmental cost of FFGUs and the frequency regulation service (FRS) cost of REG units are considered in this research for market operators to determine the scheduling priorities of submitted bids from generation units. In the frequency regulation service market model, a dynamic approach to determining the FRS demand is applied, which not only incentivizes REG units to initiatively manage their outputs, but also derives the price signals through the equilibrium of the FRS demand and supply to ensure the revenue of FFGUs in the FRS market. Meanwhile, the decision-making model of the FFGUs under the proposed market mechanism is also presented. Finally, extensive numerical experiments demonstrate the feasibility and efficiency of the mechanism. Simulation results show that the proposed mechanism can stimulate a smooth transition of FFGUs by adapting to actual situations and policy changes. [ABSTRACT FROM AUTHOR]

• A novel Wasserstein-based distributionally robust method for voltage risk assessment is proposed. • A new risk management model is proposed for unbalanced DSs, which is formulated as a hierarchical multi-objective structure. • Multiple uncertainties in different levels and objectives are formulated by the corresponding Wasserstein ambiguity sets. • Hierarchical multi-objective DRO model is reformulated into tractable bilevel mixed-integer convex programs. The large-scale integration of renewable energy generators (REGs) is key to reducing carbon emissions. However, the intermittency of REG outputs poses operational risk challenges on distribution systems (DSs). This paper proposes a novel Wasserstein-based distributionally robust assessment for the voltage risk index that represents both likelihood and non-linearly increasing severity of voltage violations. We propose a hierarchical DRO model for risk management that integrates a multi-objective distributionally robust optimization (DRO) at the distribution system operator (DSO) level and a basic DRO at the user level. Then, a systematic solution method is developed, where the proposed model is equivalently reformulated into tractable bilevel mixed-integer convex programs. The effectiveness, advantages, and scalability of the proposed method are validated on the IEEE 33-bus system and unbalanced IEEE-123-bus system. [ABSTRACT FROM AUTHOR]

• A transfer and contrastive learning architecture identifying multi-label loads. • Contrastive learning architecture to extract deep features. • Transfer learning design solves the sparsity of multi-label appliances. • Gramian angular field encoding enhances the feature extraction efficiency. • Verifications on both public datasets and real-world measurements from China. To achieve the goal of peaking carbon emissions globally and carbon neutrality, smart energy management is a promising way to boost energy conservation and estimate the residential potential for regional demand response, among which the non-intrusive load monitoring technique is highlighted due to its effectiveness on the residential side. However, the identification of multi-label appliance switching operations is still a challenge in this field, which may critically affect the total identification results due to the few-shot learning problem and the complicated overlap of features belonging to different appliances. Therefore, this paper proposed a transfer and contrastive learning architecture to identify multi-label appliances effectively. In the first stage, Gramian angular field encoding is implemented to visualize power sequences to highlight the correlation between timestamps and enhance the feature extraction efficiency. Secondly, a contrastive learning architecture is implemented to learn the general distinguishing features between samples of different labels, and density-based spatial clustering of applications with noise clustering is utilized to detect multi-label samples. Thirdly, transfer learning is utilized to enhance the multi-label identification capacity of contrastive learning structures based on the existing trained model. Finally, the effectiveness of the proposed algorithm is verified through two low-frequency non-intrusive load monitoring public datasets and real-world measurements from a pilot project in China. The results show that the proposed architecture can achieve the efficacy of deep features extraction and few-shot learning in identifying multi-label appliance switching operations. [ABSTRACT FROM AUTHOR]

• Proposing a short-term wind power forecasting model based on VMD-GRU to mitigate the impact of wind power uncertainty. • Implementing refined P2G two-stage operation to investigate the advantages of hydrogen energy efficiency. • Constructing a thermoelectric integrated demand response model with adjustable thermoelectric ratio to reduce load peak-valley differences. • Building a low-carbon economic scheduling model for IES containing electric hydrogen production based on the VMD-GRU short-term wind power prediction model. The integration of energy systems (IES) enhances the interaction between the electricity, gas, and heat systems, and the concept of low-carbon development can further reduce the carbon emissions of IES. However, the uncertainty of wind power output and the complexity of different energy chains pose significant challenges to the low-carbon operation of IES. Therefore, this paper proposes a low-carbon economic dispatching strategy for IES containing electric hydrogen production based on short-term wind power prediction. Firstly, a variational mode decomposition- gate recurrent unit network prediction model is employed to enhance the accuracy of ultra-short wind power forecasting, which reduces the impact of wind power output uncertainty on wind power grid connection. Secondly, a refined two-stage P2G operation model for refining the electric hydrogen production device is constructed to decrease the carbon emissions of IES. Thirdly, a thermoelectric integrated demand response model is established to adjust the heat and power demand proportion, further reducing the IES's carbon emissions. Finally, the case study is performed based on IEEE standard system to verify the effectiveness of the proposed strategy. Simulation analysis shows that introducing electrolysis for hydrogen production can reduce IES carbon emissions by 12.90%. In addition, introducing an adjustable thermal-electric comprehensive demand response can reduce IES carbon emissions by 1.543% while lowering the overall cost by 5.24%. The proposed strategies can simultaneously consider the low-carbon and economic aspects of IES. [ABSTRACT FROM AUTHOR]

• A Stackelberg game model is developed to coordinate the energy transactions between the operator and MVPP. • A dynamic update algorithm of sampling points to correct the Kriging metamodel is proposed. • The Stackelberg game strategy can significantly decrease the operation costs and fully exploit the carbon reduction potential of MVPP. • A dual-compensated DR mechanism can provide a better motivation to reduce carbon emissions. With the improvement of the electricity-heat-carbon trading mechanism, it has been a trend for multiple virtual power plants (MVPP) to participate in the market competition. Firstly, in order to mitigate the burden of environmental pollution and balance the conflicting market interests of MVPP, this paper proposes a Stackelberg game strategy for the operator and MVPP considering a ladder-type carbon price mechanism and a dual-compensated demand response (DR) mechanism, which consists of an optimal pricing strategy for the operator and an optimal energy scheduling strategy for each VPP. During the game process, the operator serves as the leader, guiding the energy transactions of MVPP through price optimization, while each VPP acts as the follower, optimizing the internal aggregated units and loads based on the price issued. To solve the Stackelberg game equilibrium, a Kriging metamodel is then adopted to fit the energy scheduling model of the VPP. Finally, several cases are analyzed to verify that the proposed strategy can effectively contribute to the energy complementation and carbon reduction of MVPP. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a compromise programming (CP) framework for solving a multi-objective two-stage stochastic unit commitment problem characterized by high penetration of wind power. The proposed framework aims at finding best-compromise Pareto efficient on/off schedules, accounting for wind and power demand uncertainties: such solutions must trade off the three objectives of operating cost, CO 2 emissions, and wind power curtailment in accordance to the decision maker preferences. To achieve this, we introduce a practical procedure to compute the ideal and Nadir points associated to the multi-objective two-stage stochastic unit commitment problem and propose a linearized ℓ 1 norm-based compromise program to design best-compromise on/off schedules that correspondingly minimize and maximize their weighted distances to the ideal point and to the Nadir point, considering the preference weights assigned by the decision maker to each of the three objective functions. The proposed CP framework is applied to a case study related to the New England IEEE-39 bus test system. The results show that, compared to the schedule obtained through the traditional minimization of operating cost, the designed best-compromised schedules considerably improve CO 2 emissions reduction and wind power curtailment performance by conservatively sacrificing operating cost performance. • A linear multi-objective two-stage stochastic unit commitment model is presented. • The model accounts for wind and power demand uncertainties. • The solutions must trade off operating cost, CO 2 emissions and wind power curtailment. • A compromise programming framework is proposed to solve the problem. • The obtained best-compromise Pareto efficient solutions are compared and analyzed. [ABSTRACT FROM AUTHOR]