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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]

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]

In future integrated electricity-natural gas distribution systems (IEGDS), hydrogen and methane converted from excessive renewable generation will be mixed with natural gas to form hydrogen enriched compressed natural gas (HCNG) for fewer carbon emissions. Consequently, optimal planning of the HCNG-integrated IEGDS plays an essential role in increasing the economy and technical benefits. However, integrating HCNG operation into the planning of IEGDS still has many challenges, such as unrealistic modeling, nonlinear constraints, and the potential risk of voltage violations. The purpose of this paper is to develop a tractable optimization model for planning and operation of the HCNG-integrated IEGDS, aiming to minimize the investment and operating cost, reduce the voltage violation, and improve the renewable power penetration rate. To achieve this, a bilevel optimization model is developed for optimal planning of wind turbines and soft open points (SOPs) in the IEGDS considering comprehensive HCNG operation, in which both hydrogen and methane injection are considered and optimized for different system operation scenarios. Furthermore, the proposed nonlinear model is reformulated to a tractable bilevel mixed-integer second-order cone model using several reformulation techniques and solved by the reformulation and decomposition algorithm. Case studies, performed using the publicly available datasets, are conducted to demonstrate the economic and technical improvement by implementing the proposed planning model. The annual planning results show that incorporating the coordination between SOPs and methane injection results in a 6.77% reduction in investment cost, a 22.64% reduction in operating cost, a 62.69% decrease in voltage violation, and a 23.58% increase in the wind power penetration rate. Moreover, SOPs are more applicable to the safe operation of the IEGDS under high energy load conditions. • Comprehensive HCNG operation model with both hydrogen and methane injection is proposed. • Nonlinear bilevel planning model is reformulated to its tractable form. • Benefits and performance are analyzed and demonstrated under different HCNG consumption profiles. [ABSTRACT FROM AUTHOR]

• Carbon emission flows of distribution networks are traced considering power loss and reactive power. • Individuals and the overall customers are harmonized by the proposed low-carbon allocation method at the demond side. • The decarbonization potential of shared PVs and ESSs is exploited based on CEF calculation and carbon quota mechanism. • Optimal amounts and locations of shared PVs and ESSs are obtained via the proposed method with the limited investment. With the targets of carbon peaking and carbon neutrality, carbon emission reduction measures have become significant issues in new-type power systems. Distribution networks (DNs), which multiple power sources and flexible loads access to, play an essential role in exploiting the potential of reducing carbon emissions on the demand side. To achieve a global carbon emission reduction considering the carbon quota of each customer, shared photovoltaics (PVs) and energy storage systems (ESSs) are allocated with a centralized calculation and optimization conducted by DNs. To solve two key points in demand-side planning of shared PVs and ESSs in distribution networks, i.e., the accuracy of carbon emission flow (CEF) calculation and carbon quota-oriented optimization planning, this paper proposes a low-carbon oriented planning method for shared PVs and ESSs via CEF tracing. Firstly, a novel CEF calculation method is proposed based on the forward current sweep and backward voltage sweep algorithm, which considers the tracing of reactive power and power losses. Next, carbon emission index and economic index of allocating shared PVs and ESSs are defined. Subsequently, a bi-level optimization model is presented, considering the excess carbon emissions of each load based on carbon accounting and carbon quotas. Finally, the superiority of the proposed method is verified through a modified IEEE 33-node distribution network, and the effects of various investment constraints and carbon quotas are discussed. [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]

• The papers accepted by the guest editor team of the Special Issue " Integrated Local Energy System " are reviewed. This guest editorial summarizes the topics and the papers selected for the Special Issue on Integrated Local Energy Systems (iLES). In the 35 papers approved for publication after the rigorous reviewing process, 4 papers study the local energy trading, market mechanisms and business models, 4 articles investigate the resilient and robust operation of iLES against the uncertainties and extreme events, and the potential of iLES in improving the utilization of renewables and reducing carbon emission is evaluated in 3 manuscripts. 5 papers explore the optimal energy mix and cost-benefit analysis in the iLES, and the reliability and resource adequacy of iLES are assessed in 4 papers. Further, the interactions between smart buildings, iLES and large energy systems are studied in 4 papers, and 7 papers use the big data techniques and artificial intelligence methods for iLES planning and operations. 4 papers explore the control strategies for iLES operation. The Guest Editorial Board hopes this special issue can provide a valuable information for future research and advancement in the field of integrated local energy systems. [ABSTRACT FROM AUTHOR]

• Integrated demand response mechanism (IDR) in the micro-energy grid can reduce the peak-valley difference of load, promote supply–demand balance and improve the economy of system operation. • Power to gas (P2G) and combined heat and power (CHP) devices can effectively improve the coupling relationship of electricity, gas, and heat in the micro-energy grid. • Through various devices in this paper, the inflexible operation strategy of the traditional micro-energy grid is decoupled, and the determining electricity by heat characteristic limitation of CHP units is relieved. • The simulation results show that the ladder-type carbon trading mechanism has a more potent binding force on carbon emissions and can effectively improve the environmental protection of the system. • In the process of solving, the sequence operation theory (SOT) methods can be used to solve the model quickly and accurately. The micro-energy grid can meet various load demands and realize the complementary advantages of different energy sources, which provides a new way to solve the problems of energy utilization efficiency and environmental pollution. However, how coordinating multiple energy sources and improving the flexibility of the micro-energy grid is an urgent problem to be solved. This paper proposes an optimal scheduling model based on chance-constrained programming (CCP), which considers electric vehicle (EV) charging characteristics, integrated electricity-heat demand response, and ladder-type carbon trading in the background of various renewable uncertainty. Firstly, this paper uses power to gas (P2G) technology and combined heat and power (CHP) technology to improve the flexibility of the system and realize the coupling between different energy sources. Secondly, integrated demand response (IDR) is used to explore potential interaction capabilities between electric-heat flexible load and micro-energy grid. Then, the ladder-type carbon trading mechanism is introduced in the optimization scheduling model to reduce the carbon emissions of the system. Finally, sequence operation theory (SOT) transforms the original CCP model into a conveniently solvable mixed-integer linear programming (MILP) model. The simulation results show that all subsystems are closely coupled due to the participation of P2G and CHP, which reduces the operation cost of the system by 3.9 %. The results also indicate that the IDR mechanism improves energy efficiency and reduces operating costs by 7.8 %. Finally, the results substantiate that the ladder-type carbon trading mechanism reduces the carbon emissions of the system by 18.1 % and improves the environmental benefits of the system. [ABSTRACT FROM AUTHOR]

• The optimal scheduling model for VPP to participate in carbon trading and green certificate trading is established. • The reverse search strategy and dimension mutation strategy of the proposed SCV-PSO algorithm can significantly improve the performance of the algorithm. • The proposed optimal scheduling model can significantly improve the economy and environmental friendliness of VPP operation. The grid connection of large-scale clean energy provides the possibility for the establishment of a clean energy system. The urgent problem that needs to be solved is how to improve the utilization efficiency of clean energy to reduce carbon emissions. First, the virtual power plant (VPP) operation mode under the carbon trading and green certificate trading mechanism is analyzed. Secondly, this paper incorporates carbon trading mechanism and green certificate trading mechanism into the optimal dispatch model of VPP including wind power generation, photovoltaic power generation, gas turbines and energy storage device. Taking the net profit of VPP as the optimization goal, which can take into account both economic and environmental protection. Based on whether VPP participates in carbon trading and green certificate trading, three schemes are established and compared and analyzed. In addition, to cope with the volatility of renewable energy, the utilization rates of the renewable energy output scenarios for four typical days under the three schemes were compared and analyzed. To solve this problem, this paper proposes the Self-Conclusion and Variational Particle Swarm Optimization (SCV-PSO) algorithm. The simulation results show that the VPP optimal scheduling model and solving algorithm proposed can effectively improve the utilization rate of renewable energy and reduce the carbon emission under the premise of ensuring economic efficiency. It can provide a useful reference for the low-carbon economic operation of the power system in the future. [ABSTRACT FROM AUTHOR]

• It is not possible to plan an integrated energy system systematically by adjusting parameters. • A multi-objective optimization model considering renewable energy, operation strategy and carbon emission was proposed. • Exergic efficiency was incorporated into the multi-objective equation instead of energy utilization efficiency. • The coordinated output ratio between gas turbine and power grid is a key factor affecting the overall benefits of IES. • Operation strategy of integrated energy system is related to system economy. The optimized operation of integrated energy systems (IES) is of great significance to reduce carbon emissions and improve the overall efficiency of the system, at present, IES planning lacks comprehensive planning. As a response, in this paper, a grid-connected IES is proposed, which considers the complementarity of geothermal energy and solar energy and takes heat storage into account. The multi-objective optimization problem of IES is studied for the coupling mode of electric energy, heat energy and cold energy. Taking a multi-functional park as the research object, a multi-objective optimization model was established aiming at integrating operation cost, exergic efficiency and pollution gas emission penalty cost. MO-NSGA-II algorithm was proposed to solve the optimization model, and it was compared with MOI-PSO algorithm and MO-WA algorithm. The results show that MO-NSGA-II algorithm has stronger convergence, pareto solution set is obviously better than the other two optimization algorithms, and the solution set has better continuity and uniformity. Secondly, the comparison between IES and the traditional energy supply system proved the superiority of IES in multi-objective. The cost saving rate of IES was up to 57.3%, exergic efficiency was improved 39.5% and pollution emissions were reduced 54.8%. It also proved again that MO-NSGA-II algorithm had a good effect on solving the IES model in this paper. Then, we analyzed the cold, hot and electric energy hub scheduling diagram of the three algorithms and found that MO-NSGA-II algorithm had better self-regulation ability, and the coordinated output ratio of gas turbine and power grid was a key factor affecting the overall economic and environmental benefits of IES, which was strongly correlated with the operation strategy of the system. These results can provide reference for the optimization of integrated energy system and further tap the development potential of multi-energy system. [ABSTRACT FROM AUTHOR]

• Based on the energy hub, the architecture of the park-level integrated energy system is constructed considering the coupling and conversion between multiple energy sources and the demand response on the user side. • A multi-objective robust model and opportunity model based on IGDT are established from the perspectives of risk aversion and risk preference, respectively. • Compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism can significantly reduce carbon emissions. In order to meet the challenge of global low-carbon development, the concept of integrated energy system (IES) has been proposed and demonstrated, and its low-carbon operation mode is the research hotspot at present. In this paper, a multi-objective optimal scheduling model considering the participation of park-level IES (PIES) in the carbon market is proposed, which takes into account the multiple uncertainties on the renewable energy and load. Firstly, for the PIES operator (PIESO) participate in the carbon market, this paper introduces the ladder-type carbon trading mechanism. Then, for the uncertainties of the renewable energy and load, this paper carries out uncertainty modeling from the perspective of PIESO risk aversion and risk preference based on IGDT method. Finally, a multi-objective optimal scheduling model including economic and environmental objective is established. The actual PIES is selected for case study, and the results show that the operating profit of PIESO under the risk aversion strategy decreases by 5.91%, the operating profit of PIESO under the risk seeking strategy increases by 6.12%. Aiming at the impact of ladder-type carbon trading on system operation profits and carbon emissions, three scenarios were set, including whether to introduce a ladder-type carbon trading mechanism, changes in the initial price and the tradable carbon emission ratio. The results show that compared with the traditional carbon trading mechanism, the introduction of the ladder-type carbon trading mechanism reduces carbon emissions by 2.73%, and the setting of reasonable ladder-type carbon trading price and tradable carbon emission ratio can significantly reduce the carbon emissions, improve operating profit of PIES. [ABSTRACT FROM AUTHOR]

With the goals of carbon emission reduction and carbon neutralization put forward by countries all over the world, renewable energy clusters are connected to the grid on a large scale, which leads to the problem of frequency security of the power system again. Therefore, this paper proposes a two-stage power system frequency security multi-level early warning model (FSMEWM) with DS evidence theory as a new combination strategy. The model consists of two stages: frequency security multi-level early warning and frequency security margin and risk degree prediction. In the first stage, three 1D-CNN with different structures are selected as sub-classifiers, and DS evidence theory is used as the combined strategy to integrate the results of sub-classifiers, which can evaluate whether the frequency of the system after disturbance will exceed the safety early warning limit. The second stage consists of three regression predictors, which can further give the safety margin and risk degree of early warning samples according to the early warning results of the first stage, to provide a reference basis for whether to start emergency control and what control strategy to choose. Finally, this paper takes the improved IEEE 10 machine 39 bus system as a simulation example to verify the effectiveness and computational efficiency of the model. It also shows that when taking DS evidence theory as the combination strategy, ensemble learning has better performance in early warning accuracy, early warning stability, robustness, and anti-noise ability. • This paper proposes and constructs a two-stage frequency security multi-level early warning model (FSMEWM) for power systems. The model can use time-series data sampled from the power system to give more refined multi-level frequency security early warning results. • The two-stage FSMEWM can give the frequency safety multi-level early warning level in the first stage, and the safety margin and risk degree in the second stage. It can provide a comprehensive reference for the subsequent emergency control. • Based on the ensemble learning, this paper introduces DS evidence theory as a new combination strategy for the first time. Compared with the common method, DS evidence theory has better performance in accuracy, robustness, and anti-noise ability. [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]

• A CCTS (chance-constrained two-stage) stochastic planning model is formulated to realize the planning of building IES. • Various of uncertainties and their correlation of IES are considered through HMMSG. • The benefits of ESS and V2G bidirectional interaction on low-carbon building IES are verified. • The SAA method is combined with HMMSG method to solve chance-constrained optimization problem. The low-carbon building has been proposed to mitigate the climate change caused by environmental problems and realize carbon neutrality in urban areas. In addition, the integrated energy system (IES) has been developed to reduce renewable energy curtailment in the power distribution system and improve energy efficiency due to the independent operation of traditional energy systems. In this paper, we propose a stochastic planning method for low-carbon building IES, in which the Vehicle to Grid (V2G) is also considered to further increase the flexibility of low-carbon buildings. The proposed planning method optimizes the investment and operation costs, and CO2 emission of the building IES, to achieve the maximum benefit of the low-carbon building and help realize carbon neutrality. By considering the uncertainty of distributed renewable energy, multi-energy load fluctuation and the random behavior of EV users, a two-stage stochastic programming model is formulated with chance constraints, in which the heuristic moment matching scenario generation (HMMSG) and sample average approximation (SAA) methods are applied to deal with the uncertainties. In the case study, a real IES office building in Shanghai, where photovoltaic (PV), energy storage system (ESS), fuel cell (FC), EV, etc. are included as planning options, is used as the test system to verify the effectiveness of the proposed planning method, and the functions of the ESS and EV in IES are analyzed in detail in different operation scenarios. The case study results show that the proposed planning scheme can reduce the total cost and carbon emission significantly. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

• This paper presents a novel low carbon operation framework for integrated energy systems. • A carbon constrained locational marginal price (LMP) under carbon trading scheme is introduced. • The framework is modeled as a hierarchical problem including upper supply layer and lower regional consumption layer. • Market risks are measured by applying conditional risk at value (CVaR). Coordinated operation of integrated energy system (IES) enables multiple energy carriers work flexibly and effectively to achieve both economic and environmental benefits. However, this process cannot be exploited without equitable and attractive incentives. This paper proposed a novel low carbon operation framework by introducing a carbon constrained locational marginal price (LMP) under carbon trading scheme. The framework is modeled as a hierarchical problem including upper supply layer and lower regional consumption layer. The upper layer aims to minimize total energy supply cost with extra carbon trading results, while the lower layer aims to maximize regional consumer surplus based on the LMP and local carbon credits. Carbon emission flow (CEF) model is employed to trace the carbon flow and calculate the emission at each layer. The strength of P2G facilities in emission mitigation is involved. Furthermore, market risks are measured by applying conditional risk at value (CVaR). Case study is carried out based on a modified IES including modified IEEE-24 electricity system and 19-bus gas system, the simulation results demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

• A data-model hybrid driven approach is used to model the flexibility of the charging stations. • An ADMM-based clearing mechanism considering the carbon intensity control is proposed. • The flexibility of the charging stations is verified to be used to control the carbon intensity. A large number of high-capacity charging stations are being built and will enter the wholesale electricity market as independent entities. Energy storage is considered to be an effective means of mitigating carbon emissions. When a large number of charging stations with storage capacity enter the market, how to make full use of their flexibility in the wholesale market to control the carbon intensity of the power system is an urgent problem to be solved. To address this problem, in this paper, a day-ahead market clearing mechanism for nodal carbon intensity control using the flexibility of charging stations is proposed, and a distributed clearing algorithm based on the alternating direction method of multipliers is designed. The local distribution network operators are used as intermediate coordinators, through which the market operator and the local charging station operators transmit system flexibility demands and energy schedules, respectively. Market clearing is completed through several iterations. This reduces the complexity of the unified clearing model. Moreover, based on the data-model hybrid driven approach, the generalized energy storage model of the charging stations and the linear nodal carbon intensity constraints of the power system are established. The locational marginal price considering the carbon emission cost is derived. The simulation verifies that using the flexibility of the charging stations to improve the power flow distribution of the power system can control the nodal carbon intensity with only 0.11% cost increase. The distributed algorithm has good convergence. [ABSTRACT FROM AUTHOR]

• A novel IES coordinated optimization scheduling operation model based on CCHP, carbon capture, IDR and ladder-type carbon trading mechanism is constructed. • A novel DR scheme considering cooling, heating, and electricity load satisfaction is proposed, and its impact on demand-side load is analyzed in-depth. • A carbon allowance mechanism in line with the operating characteristics of IES is proposed, and a ladder-type carbon trading cost model is constructed to reduce system carbon emissions and achieve low-carbon operation of IES economy. The low-carbon economy operation of the integrated energy system can be realized by introducing the demand response and carbon trading mechanism into the optimal scheduling of the integrated energy system. In this paper, an optimal scheduling model based on CCHP and carbon capture device is proposed, which takes into account the demand response of cooling, heating and electricity load and ladder-type carbon trading mechanism. Firstly, a multi-energy and multi-type demand response model based on time-of-use electricity price and incentive mechanism is established, and user satisfaction is used to evaluate it. Then, a carbon trading model of integrated energy system is established considering the actual carbon emissions of the system and the ladder-type carbon trading mechanism. Finally, taking the minimum sum of energy purchase cost, maintenance cost, carbon emission cost and compensation cost as the objective function, combined with the operation constraints of multi-energy flow of integrated energy system, an optimal scheduling model which takes into account both low-carbon and economy is constructed, and the problem is transformed into a mixed integer linear problem and solved by CPLEX. By setting up four scenarios for example analysis, the results show that the system total cost of the ladder-type carbon trading is decreased by 5.9% compared with the traditional carbon trading, and on the basis, the system total operating cost is decreased by 3.1% after considering the user-side DR. The simulation results further show that the introduction of DR and ladder-type carbon trading mechanism can flexibly transfer load, reduce gas purchase and reduce system carbon emissions, which has significant application value. [ABSTRACT FROM AUTHOR]

Redispatch measures are a key instrument of the Central European electricity market design to prevent congestion of several transmission lines after the market clearing. In addition to generation rescheduling, demand-side flexibility can reduce this redispatch need. This paper mainly aims to analyse the flexibility demand application of electric vehicle fleets in the redispatch market. The developed European electricity market model minimises dispatch- and subsequently redispatch costs whilst using electric vehicle fleets as flexible demand. A novel modelling approach allows the integration of this flexibility into large-scale linear programming models without losing their essential parameters. This case study is examined in Austria with a remarkably high share of electricity from renewable energy sources and different market penetrations of electromobility. Results show that integrating this system as a redispatch measure leads to a reduced curtailment of renewable energies (up to 25%), whilst less additional thermal power plant usage is needed. Furthermore, redispatch cost and the associated CO 2 emissions are reduced by 3.3% to 13.9%. By contrast, using this flexibility as a market-based charging strategy raises CO 2 emissions and redispatch costs drastically by 186% if 2 million electric vehicles are considered. Especially with the high electrification rate of the transport sector, the provided flexibility potential significantly impacts the electricity market. Further efforts could address the influence of demand-side flexibility not only on redispatch within one control area but also on cross-border redispatch and counter-trading. • Implementation of electric vehicle fleets as demand-side flexibility. • Integration of flexibility in the dispatch and redispatch of the electricity market. • Flexibility as redispatch measure reduces redispatch costs and CO 2 emissions. • Flexibility as market-based charging strategy leads to an increase in congestion. [ABSTRACT FROM AUTHOR]

A renewable energy consumption method based on variational mode decomposition and combined regulation of thermal power and storage system is presented in this paper. By decomposing the equivalent load curve using variational mode decomposition, the equivalent load curve is divided into parts with different center frequencies and energy system constraints and solved separately. We then take the maximum wind power absorption rate as the upper model target and solve it. At the same time, through the operation curves of the thermal power unit and battery storage system, the lower optimization model is established to minimize the system's operating cost with carbon emission costs. The example shows that the regulation method with a storage system improves the consumption rate of wind power and reduces carbon dioxide emission compared with the regulation method without a storage system. Compared with the method without decomposition, the power output curve of the thermal power unit is smoother under the new method, effectively improving the solving speed. It reduces the operating cost of the system and its carbon dioxide emissions. • Variational mode decomposition of equivalent load curve and analysis of its characteristics. • Upper levels model improves wind power accommodation rate. • Lower levels model reduces carbon emissions and operating costs of thermal power units. • Frequency block adjustment makes output curve of thermal power unit smoother. [ABSTRACT FROM AUTHOR]

• A dynamic planning method for the optimal design of power-to-gas integrated energy hub system is proposed. • The economic and technical feasibility of the integration of power-to-gas to the energy hub is investigated under future market conditions. • A post-combustion carbon capture unit is combined with the power-to-gas facility to reuse CO 2 from the flue gas of the CHP and boiler. • The impact of demand response programs on the performance of the power-to-gas integrated energy hub system is investigated. Power-to-gas (P2G) technology and renewable energy are widely developed and are at the edge of a mass roll-out. Still, regulatory and economic barriers are the foremost challenge facing P2G technology and renewable energy. This paper investigates the economic and technical feasibility of designing P2G within the energy hub systems under future market conditions. The design of the P2G integrated energy hub (P2G-EH) system is performed with a stochastic dynamic planning method. The proposed planning method is divided into several sub-horizon times, and the optimal size of the system components is determined in each sub-horizons. The proposed P2G system is equipped with a carbon capture unit recovering CO 2 emissions from the flue gases of the boiler and combined heat and power system (CHP). In the planning problem, the reliability constraint is considered to evaluate the quality of supplying loads. Moreover, system uncertainty parameters are modeled through the scenario-based stochastic method. The obtained results exhibit that the P2G system has positive effects on mitigating emissions by about 17.7 %, reducing operating costs by 14.6 %, and improving the reliability of thermal loads. With the future expected development of components' capital costs, the P2G system can become a profitable case for integration with the energy hub system. In addition, the implementation of the demand response programs (DRPs) besides the P2G system has a beneficial impact on system performance and reduces the operation and investment costs of the P2G-EH system by 4 % and 24 %, respectively. [ABSTRACT FROM AUTHOR]

• The IES cloud-edge collaborative distributed dispatching architecture is proposed. • Based on PJ-ADMM, the IES distributed optimal solution method is proposed. • Considering privacy protection in IESs, subproblems can be solved in parallel. • Compared with centralized methods, the proposed method shows higher solution efficiency. • The impact of demand response on IES operation and carbon trading is analyzed. To reduce carbon emissions and improve the energy efficiency of energy systems, integrated energy systems (IESs) have been promoted in recent years. However, the data of different energy networks cannot be fully shared. The data privacy protection is attracting more attention in demand side. It is difficult for centralized dispatching strategies, which must obtain all the data of energy systems during optimization, to be applied. To solve these issues, a cloud-edge collaborative distributed optimal dispatching strategy for electric-gas IESs is proposed in this paper. The centralized IES scheduling problem is reasonably divided into multiple subproblems. Considering information barrier, the cloud computing centers are separately set in different energy networks to solve energy flow optimization subproblems. Considering prosumer privacy protection, edge computing units are separately set in energy hubs to deal with cost and carbon emissions minimization of regional integrated energy systems (RIESs). Based on the proximal Jacobian alternating direction method of multipliers, the common optimization variables interactive iteration and parallel solution of multiple dispatching models are developed. The simulation results show that the proposed distributed optimization method can achieve the same accuracy as the centralized optimization method and improve problem-solving efficiency in general. [ABSTRACT FROM AUTHOR]

In this paper combined demand side management strategy for residential consumers is studied for five households in South Africa. This study is twofold; the first part proposes an energy management system that combines demand side management strategies with a view of minimizing the consumer’s cost and reducing the power consumption from the grid. Appliance scheduling with a dedicated photovoltaic and storage system under time-of-use tariff shows that customers can realize cost savings and the power demanded from the grid is reduced by optimal scheduling of power sources. In the second part of this study, a model is developed to investigate the joint influence of price and CO 2 emissions. It is found that CO 2 emissions could give customers an environmental motivation to shift loads during peak hours, as it would enable co-optimization of electricity consumption costs and carbon emissions reductions. It is also demonstrated that the consumer’s preferences on the cost sub-functions of energy, inconvenience and carbon emissions affects the consumption pattern. These results are important for both the consumer and the electricity suppliers, as they illustrate the optimal decisions considered in the presence of trade-offs between multiple objectives. A further study crucial to the consumer on economic analysis of PV and battery system showed that the consumer could recoup their initial investment within 5 years of their investment. [ABSTRACT FROM AUTHOR]

• Proposing a stochastic scheduling framework to simultaneously optimize both energy hubs and distribution networks consisting of electricity distribution network and gas distribution network in large-scale multi-energy hub systems which integrate the renewable energy sources. The objective function is to minimize the cost terms of energy and operation as well as cost terms related to emissions of the system. The energy forms can be converted, conditioned, and stored to optimize the operation of both energy hubs and distribution networks. • Considering the uncertainty of output power of renewable energy sources, energy demand of consumers, and electrical price along with demand response programs in the optimal model. • Calculating and integrating the energy loss of the device together with electricity distribution network and gas distribution network into the operation framework by nonlinear constraints in which electricity distribution network employs the AC power flow model. • Evaluating the feasibility of the optimal operating model and the efficiency of multi-energy hub systems with different structures of energy hubs and the impact of demand response programs. The optimal operation of multi-energy systems based on energy hubs (EHs) is a significant challenge for operators due to the mutual impact of different energies and the uncertainty of renewable energy sources, electricity prices, the energy demand of consumers. To address this challenge, a stochastic scheduling framework for multi-energy hub systems is proposed in this paper to simultaneously optimize both EHs and distribution networks. The proposed framework considers RESs, uncertain parameters, DRPs, and emission to ensure system operation in fact. Furthermore, the uncertainty of electrical, heating, and cooling demands, electrical prices, and output power of RESs is modeled by probability density functions. They are divided into states by clustering technique to form the scenario matrix. A scenario reduction technique is then employed to reduce the number of scenarios to 10 to reduce the computational burden. The objective of the proposed framework is to minimize the total operation cost of the system, which is modeled as a mixed-integer nonlinear programming (MINLP) problem. The numerical results from different case studies show that the operation costs of the multi-energy systems using the proposed method are reduced by 2.0–14.5% among different analyzed cases. Additionally, CO 2 emission is reduced by 1.9% and 22.3% considering DRPs and RESs, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • Indian electricity demand reduced by 11 % vis-à-vis 2019 during the lockdown. • Sectoral demand reduction—73 % industrial, remaining 27 % commercial. • Pan India lights-off event—hydropower provides the bulk of the flexibility (60 %). • The plant load factor of coal dropped to a record low of 35 % during phase-1. • Power sector emissions reduced by 16 % (86 MtCO2) compared to 2019. This paper aims to understand the responsiveness of the power sector during the lockdown due to the COVID-19 pandemic beginning from March 25, 2020 and analyses its impact on the demand, operation and supply of electricity in the Indian power system. The role of the C&I load share in reducing the electricity demand of the different states in India has been examined using multi-sectoral regression analysis. The impact of the pan India lights-off event on the short-term operational flexibility response of the power system has also been analysed using high-temporal resolution data. The results indicate that there has been a reduction of nearly 70 TWh of electricity demand during the lockdown period, an 11 % reduction compared to 2019. The top three states recording the highest reduction were Maharashtra, Gujarat and Tamil Nadu at 11 TWh, 10.6 TWh and 8.4 TWh, respectively. Regression analysis revealed that out of the total drop in load demand — 73 % is owed to the industrial sector, while the remaining 27 % is to the commercial sector. This demand drop also impacted the upstream electricity supply mix where 96 % of the reduction in supply was borne by the coal generating units, recording its lowest national plant load factor at 35 % with a 16 % reduction in overall CO 2 emission compared to the same period in 2019. In conclusion, a case study of Maharashtra has been used to analyse the impact of this reduction in electricity demand on the supply mix, hourly load profile and cost of supply under the different lockdown phases. [ABSTRACT FROM AUTHOR]

• The energy diversity and loads complementarity of a university are considered. • The multi-energy trading realizes energy sharing among different stakeholders. • The auction bases on the usage demands of secondary energies and carbon emission. • This bi-level EPEC model of non-cooperative game is solved by PSO algorithm. • The relation between prices and quantities is automatically established. With the increasing deployment of distributed energy resources (DERs) and operation of diverse energy devices, different types of multi-energy systems (MESs) emerge. Constrained by physical networks, when economic and environmental benefits are considered, how to stimulate the potential of energy shifting and conversion across temporal and spatial dimensions becomes a significant issue. This requires an innovative multi-energy trading mechanism. Due to the diversity of building types and energy categories, as well as the loads complementarity, the university community is an applicable user cluster for the investigation of multi-energy trading. The district energy operator (DEO) contributes to making deals via the interactive information of prices and quantities, besides protecting the prosumers' privacy. This paper treats carbon as a direct trading object and proposes an internal multi-energy trading mechanism, which adopts an auction based on the demands for cooling, heating, electricity and carbon, and is formed as a nonlinear equilibrium program with equilibrium constraints (EPEC) problem. The nonlinear EPEC problem is then solved by particle swarm optimization (PSO) algorithm. It is a novel mechanism in which the prosumers submit prices and quantities simultaneously, instead of utilizing traditional linear supply functions. A hypothetical university community in the U.K. is studied to discover the internal price signals. Simulation results show that, the proposed trading mechanism benefits from exploiting the energy sharing potential of the community MES, as well as improving the players' revenues. Meanwhile this mechanism presents good performances when tackling the infrequent load growth, and facing the change of exterior market prices or rules. [ABSTRACT FROM AUTHOR]

Abstract: Recently hybrid power systems (HPSs) consisting of integrated operation of two or more different types of energy sources and storage devices are being deployed for rural electrification or electrification of remote areas in many countries across the world. This is seen as a cost effective solution in contrast to extending the utility grid in remote areas. The types of upcoming renewable and low-carbon generation technologies are being preferred for such systems depending on their availability and economic viability in these countries. Moreover, HPSs are also being preferred in order to improve the overall energy efficiency of the system and to get a judicious generation mix which would minimise the operating cost of the system. Rural communities in South Africa endure poor access to electricity mostly due to the lack of grid connected power lines. It is therefore the ideal place to conduct a study on the economic feasibility of introducing HPSs for typical residential loads for the rural community in South Africa. This paper reports on the investigating economic feasibility of a PV/diesel HPS in various climatic zones within South Africa. [Copyright &y& Elsevier]

• A day-ahead generation and HVDC transmission joint scheduling method is presented. • Operation cost, carbon and pollutant emission are considered in objective function. • A distributed scheduling model is established based on SADMM technique. • The normal boundary intersection method is used to handle multiple objectives. • Case studies of a two-area HVDC-linked power grid show the method's effectiveness. In order to improve the overall operation economy and efficiency of two-area HVDC-linked power grids, a distributed scheduling approach is presented in this paper for the multi-objective day-ahead generation and HVDC transmission joint scheduling (GHT-JS) problem. At first, a multi-objective day-ahead GHT-JS model is formulated aiming at minimizing the operation cost, the carbon emission and the pollutant emission, in which HVDC tie-line operation constraints are considered. Secondly, the normal boundary intersection method is implemented to transform the multi-objective GHT-JS problem into a series of single-objective GHT-JS problems. Then, a distributed scheduling model is established based on the synchronous alternating direction method of multipliers for each single-objective problem. The original problem is decomposed into an upper-level master sub-problem of coordinating HVDC transmission power and two parallel lower-level generation scheduling sub-problems. Finally, case studies of a two-area interconnected test system via two HVDC tie-lines show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

• Economic dispatch model is built for multi-area electricity and natural gas systems. • Distribution of wind energy and power transmission congestions are considered. • Maximum allowable emission bounds are introduced to restrict gas emissions. • Hourly spinning reserves are allocated for both electricity and natural gas systems. With the increasing growth of gas-fired units and power-to-gas facilities, the power system and natural gas system become an integrated electricity and natural gas system (IENGS), which brings new opportunities to mitigate the associated environmental issues and deal with various uncertainties from electricity load, renewable energy generation, and gas load. Different geographical locations may have many IENGSs which are connected by electric or gas transmission lines, thus formulating multi-area IENGSs. These multi-area IENGSs are not simple addition of individual IENGSs, since renewable energy may distribute unequally between different IENGSs, and excess renewable energy needs to be transmitted from one area to another, but is limited by potential tie-line congestions. To solve these problems, this paper proposes an economic dispatch model for multi-area IENGSs considering tie-line congestion, maximum allowable emission bounds and hourly spinning reserve constraints. The considered multi-area IENGS consists of multiple single-area IENGSs, among which wind energy distribution varies considerably. The maximum allowable emission bounds of carbon dioxide and nitrogen oxygen are separately set based on their different influences on the environment. The hourly spinning reserves are allocated for both the power system and the natural gas system, which are provided by fossil fuel-fired units, power-to-gas facilities, gas pipelines, and natural gas storage facilities. After that, the presented model is converted into a mixed-integer linear programming problem for higher computing efficiency. Case studies for integrated systems are analyzed to demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

• A pricing model for renewable energy certificates is proposed. • The combination of the REC market and ETS can promote emission reduction. • The cooperation of renewable energy plants and P2GSes increases the profit of collision. To reduce carbon emissions in the power sector, the emissions trading system (ETS) is widely used to mitigate emissions from thermal generators. Also, large-scale and distributed renewable energy is invested to replace conventional thermal generators. Renewable energy certificates (RECs) are designed to support the prior dispatch of renewable energy. However, the purchase motivation for RECs is worth attention. In this paper, a theoretical methodology is proposed to promote transactions of RECs. The cooperation of renewable power plants and the emerging storage technology, e.g., power-to-gas (P2G), is considered to further reduce emissions. In the proposed bi-level model, which is based on the game approach, the thermal generators can purchase RECs and convert them to carbon emission quotas. The oligopoly market is considered to reveal the real payoffs of thermal generators, renewable power plants, and power-to-gas stations (P2GSes). The proposed model is verified on the IEEE 30-bus system. It is found that the RECs can help renewable energy plants increase income by 19.4% without considering cooperation, and more renewable energy is dispatched as priority. It can be concluded that the cooperation of P2GSes and renewable plants considering the RECs can increase the collision payoffs and smooth the output of renewable energy. Sensitive analysis reveals the interaction between the carbon market and the RECs market. [ABSTRACT FROM AUTHOR]

• A novel stochastic bi-level strategic bidding model for P2G facility is proposed. • Carbon emission trading scheme (CETS) and its impact on LMP are considered. • The bi-level model is converted into mixed-integer linear programming (MILP). • The proposed bidding model can increase the profit of P2G facility. • CETS can increase the profit of P2G facilities and reduce carbon emission. As an emerging energy storage technology for renewable power utilization, power-to-gas (P2G) requires efficient bidding strategies to increase profit. This paper proposes a bi-level strategic bidding model for P2G with the consideration of a carbon emission trading scheme (CETS), an embedded locational marginal price (LMP) and wind power uncertainty, where the upper level model aims to maximize the profit of P2G facilities and the lower level model carries out the market clearing process. Under a CETS, a P2G facility purchases electricity power according to the LMP and is assumed to sell synthetic natural gas (SNG) and carbon emission permits for revenue. The electricity market clearing process is represented by a stochastic low carbon economic dispatch (ED) model considering the CETS, P2G and wind power uncertainty. On this basis, the formulation of a CETS embedded LMP (CETS-LMP) is presented and analyzed here. The bi-level model is reformulated and converted into mixed-integer linear programming (MILP) using strong duality theory. Case studies performed on a modified PJM 5-bus system and an IEEE 118-bus system verify the effectiveness of the proposed model and demonstrate that considering the CETS can increase the profit of P2G facilities and reduce carbon emission. [ABSTRACT FROM AUTHOR]

• The structure of EHGHS is established, and a two-stage P2G technology is proposed. • An improved spectral clustering scenario reduction method is presented to describe the uncertainties. • The EHGHS stochastic optimal dispatching model is more effective in terms of cost saving and renewable utilization. The couplings and interactions among the multi-energy resources in the integrated energy system (IES) significantly improve the utilization of renewables and reduce carbon emissions. Hydrogen is considered to be one of the most potential energy carriers due to its excellent characteristics. Therefore, the use of hydrogen has become a hot spot in the energy field. Most researches on power to gas (P2G) technology do not use the potential advantages of hydrogen but only analyze the coupling relationship between electricity and natural gas, so the efficiency is low. Moreover, the uncertainties of renewable energy and load bring challenges to the power system dispatching. To solve these problems, an electricity-hydrogen-gas-heat integrated energy system (EHGHS) stochastic optimal dispatching strategy based on improved spectral clustering method is presented in this paper. First, the structure of EHGHS and the energy conversion unit in the EHGHS is modeled. A two-stage P2G technology is proposed which exploits the hydrogen utilization process and the combined heat and power generation process. Then a scenario reduction method based on improved spectral clustering is presented to describe the uncertain characteristics of renewable energy and load. The curve distance and cosine similarity are developed to represent the similarity between scenarios. Finally, the effectiveness, economics, and sensitivity of the stochastic optimal dispatching model are verified by case studies. [ABSTRACT FROM AUTHOR]