A low-carbon economic dispatch model incorporated with consumption-side emission penalty scheme

DRL based low carbon economic dispatch by considering power transmission safety limitations in internet of energy

A low-carbon economic optimization dispatch model of integrated energy system is proposed to improve the low-carbon and economic efficiency of the integrated energy systems. Firstly, the waste heat generator with the organic Rankine cycle is introduced into the combined heat and power to decouple the combined heat and power operation, and a coupled model with an organic Rankine cycle, power to gas, combined heat and power and carbon capture system is established. Then, the ladder-type carbon trading mechanism is introduced to improve the low-carbon model. Finally, the function is established to minimize the sum of energy purchase costs, operation and maintenance costs, and environmental costs. The proposed integrated energy systems’ low-carbon economic dispatch model reduces the total operating cost by 18.9% and the carbon emissions by 83.7% by setting up different models for comparative analysis.

The trend of low-carbon power puts forward new standards and challenges for the operation of power system, and also makes the optimal dispatching of power system more difficult. In order to better realize the low-carbon economic operation of the power system, this paper proposes a bi-level low-carbon economic dispatching (LCED) model for multi-regional power system. In the upper level, a carbon emission constraint based on event-driven mechanism tailored for coordinated operation of multiple regions is proposed, and the optimal objective is to minimize the total operating cost of the system. In the lower level, the reduction of carbon emission obligation on the load side is considered, and the optimal objective is to maximize the low-carbon utility on the load side. Thus, the synergy of the low-carbon goal on both source and load sides is achieved. Finally, through the simulation of the modified IEEE 39-bus system, it is verified that the proposed bi-level model can effectively reduce operating costs and reduce carbon emissions, and improve the level of renewable energy consumption.

To reduce carbon emission levels and improve the low-carbon performance and economic efficiency of Integrated Energy Systems (IESs), this paper introduces oxy-fuel combustion technology to transform traditional units and proposes a low-carbon economic dispatch method. Considering the stepwise carbon trading mechanism, it provides new ideas for promoting energy conservation, emission reduction, and economic operation of integrated energy systems from both technical and policy perspectives. Firstly, the basic principles and energy flow characteristics of oxy-fuel combustion technology are studied, and a model including an air separation unit, an oxygen storage tank, and carbon capture equipment is constructed. Secondly, a two-stage power-to-gas (P2G) model is established to build a joint operation framework for oxy-fuel combustion and P2G. On this basis, a stepwise carbon trading mechanism is introduced to further constrain the carbon emissions of the system, and a low-carbon economic dispatch model with the objective of minimizing the total system operation cost is established. Finally, multiple scenarios are set up for simulation analysis, which verifies that the proposed low-carbon economic optimal dispatch strategy can effectively reduce the system operation cost by approximately 21.4% and improve the system’s carbon emission level with a total carbon emission reduction of about 38.3%. Meanwhile, the introduction of the stepwise carbon trading mechanism reduces the total cost by 12.3% and carbon emissions by 2010.19 tons, increasing the carbon trading revenue.

Carbon tax is an important regulatory approach for the decarbonization of the power system. However, with the continuous increase of the penetration rate of renewable energy in the power system, it is important to research how carbon tax pricing levels affect power system carbon emission reduction. To this end, an economic dispatch model of power system that considers the carbon tax and wind power uncertainty is proposed. The model is optimized to minimize carbon emissions costs, power generation operating costs, and generator fixed costs. This paper studies the promotion effect of carbon tax on power system carbon reduction under various wind power penetration rates. Based on the modified IEEE 39-bus power system and MATLAB/YLAMIP simulation platform with Gurobi solver, the numerical analysis of cases show that the proposed model can effectively promote the system to reduce carbon emissions. In addition, the optimal carbon tax pricing levels under different wind power penetration rates can be derived. This provides a theoretical reference for formulating reasonable carbon tax pricing levels to better promote decarbonization in power systems with different wind penetration rates.

Low-carbon economic dispatch of electricity-gas systems

The power generation process in thermal power plants releases significant amounts of carbon dioxide, contributing to environmental pollution. To address the environmental challenges in power generation, the power system must evolve toward higher efficiency and reduced emissions. Carbon capture power plants (CCPPs) and wind power are expected to play increasingly important roles because of their cleaner energy characteristics. However, the high energy consumption of CCPPs and the complexity of scheduling models limit the development of low-carbon economic scheduling. To address these issues, a low-carbon economic dispatch method is proposed to coordinate the operation of wind power and CCPPs. First, a wind‒fire low-carbon economic dispatch model with CCPPs is constructed. Second, an archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) algorithm is proposed, with static scheduling results used as initial guidance particles for solving a carbon capture plant’s wind‒fire low-carbon economic dispatch model. Finally, a constraint-handling method is proposed. It repairs infeasible solutions outside the solution region to bring them within the feasible region, offering more intermediate solutions for the AS-MOPSO algorithm. The simulation results of the IEEE 39-node system show that the proposed method reduces the dispatch system’s carbon emissions and generation cost. Compared with the comparative method, the carbon emissions of AS-MOPSO decreased by about 2000 tons. A scheduling model that balances power generation costs and carbon emissions. This study proposes a wind fire joint scheduling model that considers power generation costs and carbon emissions. In order to effectively utilize wind power generation and improve the coordinated operation capabilities of carbon capture power plants, wind power plants, and thermal power plants, a flexible energy coupling relationship model between these power sources has been established. This model balances the contradiction between power generation costs and carbon emissions targets through multi-objective optimization. Prioritize the use of discarded wind power for carbon capture. Utilize the discarded amount of wind power during the scheduling process to supply energy to carbon capture equipment, in order to improve the overall wind power utilization rate of the system. Afterwards, through the carbon trading mechanism, profits were obtained by selling carbon quotas, reducing the power generation costs of carbon capture power plants. By utilizing the carbon trading mechanism, guide power plants to develop towards low-cost and low-carbon emissions. Power dispatch solutions with lower costs and less carbon emissions. This study proposed archive self-learning-based multi-objective particle swarm optimization (AS-MOPSO) Algorithm. The proposed AS-MOPSO algorithm uses the static scheduling results of each time period as initial guiding particles to reduce the blindness of the search in the early stage of the optimization algorithm, accelerate the convergence speed of the algorithm, and increase the number of high-quality solutions. The AS-MOPSO algorithm can provide scheduling solutions with lower power generation costs and less carbon emissions.

In order to optimize the total operation costs of the integrated energy system (IES) and reduce carbon emissions, a low carbon optimal dispatch model is proposed, in which both of the low carbon and economic efficiency are taken into account. Firstly, a model of integrated energy system is established, which includes coupled components such as electricity to gas (P2G), gas-fired cogeneration units (gas-fired CHP) and gas-fired boilers (GB), etc. The carbon trading mechanism is introduced into the dispatch model of the system, and an improved stepped carbon trading model is established, in which the carbon trading price is set by carbon emission intervals. A low carbon economic dispatch model for the electricity-thermal-gas interconnection energy system is then established, taking into account the carbon trading costs and system operation costs. Finally, the dispatch results of four dispatch models are compared and analyzed to verify the effectiveness of the proposed model.

The power system needs to coordinate the economic dispatch and the carbon emission under the carbon market. The CO 2 emission characteristics of different generation resources arc analyzed, then generation resources are divided into three categories: high , low and zero carbon emission. Considering the carbon capture unit and the demand response, the Low-Carbon Economic Dispatch model is established with carbon trading cost included in the objective function. To take the uncertainty of carbon price caused by multiple uncertain factors into consideration, a new low-carbon economic dispatch model is built, which uses Conditional Value-at-Risk(CVaR) method to recalculate the carbon trading cost. The YALMIP software is applied to solve the proposed model. The case studies show that low-carbon economic dispatch can improve the utilization of low-carbon and zero-carbon generation resources under carbon trading framework and the dispatch results are affected by the changes of carbon price greatly. Furthermore, with confidence level of carbon price uncertainty rising, the dispatch costs and carbon emissions increase accordingly.

To realize the integrated energy system (IES) low-carbon and economy dispatches and renewable energy utilization, the integrated energy system economic dispatch model introduces the liquid carbon dioxide energy storage (LCES) and carbon capture system (CCS). This paper proposes a low-carbon economic dispatch model for an integrated energy system that considers LCES and carbon capture system. The paper considers the impact of carbon trading mechanisms on systemic carbon emissions, aims to minimize the total operating cost of the system, and comparison of integrated energy system dispatch for two scenarios: integrated energy system equipped with LCES and integrated energy system equipped with battery energy storage. CPLEX simulation software simulates this comprehensive energy system. Analyzing the dispatching results from different perspectives, such as electric energy, thermal energy, and CO2 emissions. These results show that the proposed model effectively reduces carbon emissions, improves energy utilization, and achieves comprehensive low-carbon economic operation of the integrated energy system.

Multiple energy systems enable a deep interconnection between power system and heat system and provide an effective way for low‐carbon development. Here, a low‐carbon economic dispatch (LCED) model for integrated heat and power system is proposed. The emission characteristics of different generation units are modelled. The objective function is to minimise the total cost, including the operation cost and carbon emission cost. Numerical results based on an integrated systems including a modified IEEE RTS‐79 system and a seven‐node heating system show the effectiveness of the proposed model. The effects of carbon price on operation conditions have been further analysed.

Reducing carbon emissions is an important goal of environmental protection, and the high penetration of wind power contributes to energy conservation and emission reduction. With the rapid development of electric vehicles, it has become a new research direction to incorporate them into the flexible resources to participate in low-carbon economic scheduling. Firstly, this paper studies the impact of wind power and electric vehicle synergy on carbon emissions, and gives the calculation method of carbon emissions per unit electricity; Then, considering energy storage, a low-carbon economic dispatch model is established to minimize fuel cost and carbon emissions. The simulation results in IEEE 30 bus system show that the proposed model and algorithm can improve the utilization rate of wind power, and the effect of energy saving and emission reduction is remarkable.

Comparing CO2 emissions impacts of electricity storage across applications and energy systems

To address the issues of carbon trading cost allocation and emission reduction incentives in integrated energy system, a low-carbon economic dispatch strategy considering multi-agent green certificate-carbon trading bidirectional interaction and cost allocation is proposed. First, by introducing green certificate trading and a tiered carbon emission trading mechanism, a bidirectional interaction model for green certificates and carbon quotas is constructed, and the low-carbon value of electricity-heat-hydrogen hybrid energy storage is quantified. Second, a multi-agent carbon trading cost allocation model is designed, and a leader-follower game model is established with energy marketer as the leader and energy supplier and load aggregator as followers. Dynamic time-of-use carbon pricing is used to guide the optimization of equipment output and energy consumption strategies. On this basis, a triple incentive strategy based on electricity price incentives, green certificate revenue, and carbon trading compensation is proposed to enhance the economic feasibility of electricity-heat-hydrogen hybrid energy storage. Additionally, a lifespan degradation model is established to more accurately evaluate its long-term operational costs.Research findings indicate that adopting a dual-interaction mechanism combining green certificates and tiered carbon trading reduced the comprehensive operating costs of IES by 1.72% and lowered carbon emissions by 0.53%. The triple incentive strategy increased the operational revenue of the electricity-heat-hydrogen hybrid energy storage system by 76.16%, while improving the utilization rates of wind and solar power generation by 1.26% and 2.6%, respectively. The multi-party carbon trading cost-sharing mechanism boosted the system's overall total revenue by 3.71%, reduced total costs by 11.97%, lowered carbon trading costs by 21.06%, and decreased total carbon emissions by 19%. • A novel GCT–CET bidirectional mechanism quantifies the low-carbon value of electricity-heat-hydrogen hybrid energy storage. • Master–slave game with dynamic TOU carbon pricing guides energy dispatch and allocates multi-agent carbon trading costs. • Electricity price, GCT, and CET incentives drive hybrid storage to improve renewable integration and peak-valley regulation. • The strategy cuts system carbon emissions by 19% and lowers operational costs by 11.97% versus conventional methods. • A lifetime degradation model for electrolyzers and batteries enables more accurate long-term system cost assessment.

This paper investigates Regional Integrated Energy Systems (RIES), emphasizing the connection of diverse energy supply subsystems to address varied user needs and enhance operational efficiency. A novel low-carbon economic dispatch method, utilizing the multi-objective chaotic artificial hummingbird algorithm, is introduced. The method not only optimizes economic and environmental benefits but also aligns with "carbon peak and carbon neutrality" objectives. The study begins by presenting a comprehensive low-carbon economic dispatch model, followed by the proposal of the multi-objective chaotic artificial hummingbird algorithm, crucial for deriving the Pareto frontier of the low-carbon economic dispatch model. Additionally, we introduce a TOPSIS approach based on combined subjective and objective weights, this approach harnesses the objective data from the Pareto solution set deftly, curbs the subjective biases of dispatchers effectively and facilitates the selection of an optimal system operation plan from the Pareto frontier. Finally, the simulation results highlight the outstanding performance of our method in terms of optimization outcomes, convergence efficiency, and solution diversity. Noteworthy among these results is an 8.8% decrease in system operational economic costs and a 14.2% reduction in carbon emissions.