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Renewable energy and energy storage are essential technologies for decarbonizing energy systems. Expansion planning of the two technologies considering source‐side carbon responsibility has been well studied. However, expansion planning considering both source‐side and load‐side carbon responsibility, which may simultaneously stimulate the carbon reduction potential of source and load, has been rarely studied. To fill this research gap, this paper proposes a bi‐layer wind turbine generator (WTG) and demand‐side battery (DSB) coordinated planning framework considering wind power and load uncertainties. Moreover, a novel source‐load bilateral carbon trading (BCT) mechanism based on the Aumann–Shapley method is proposed to stimulate the WTG and DSB configuration. In the bi‐layer planning framework with BCT mechanism, WTG and DSB capacity planning with the two‐stage stochastic optimization are conducted in the upper and lower layer, respectively. Finally, test results on a modified IEEE 24‐bus system demonstrate that the proposed framework with BCT mechanism can effectively motivate the planning configuration of WTG and DSB and carbon mitigation. Compared with the models with unilateral carbon trading mechanisms, the proposed model has advantages in stimulating configuration of WTG and DSB, reducing carbon emissions, and balancing source‐load economic burden from carbon trading. [ABSTRACT FROM AUTHOR]

Introducing carbon trading is an essential way to decarbonize the power system. Many existing studies mainly consider source-side unilateral carbon trading (UCT). However, there are still rare studies considering source-load bilateral carbon trading (BCT). The effect of source-load BCT on system-wide carbon mitigation is worth studying. To fill this research gap, a hierarchical low-carbon economic-dispatch model with source-load BCT based on the Aumann–Shapley method was proposed. In the first layer, economic-dispatch was conducted to minimize the power-generation costs and source-side carbon-trading costs. Then, based on the carbon-emission flow (CEF) theory, the actual load carbon emissions can be obtained and passed to the second layer. At the second layer, the demand-response optimization was performed to minimize the load-side carbon-trading costs. Finally, the proposed model was tested on the modified New England 39-bus and IEEE 118-bus systems using the MATLAB/YALMIP platform with the Gurobi solver. The results indicate that the proposed model can effectively facilitate peak-load shifting, wind-power consumption, and carbon mitigation. Furthermore, compared with the models only considering source-side or load-side UCT, the proposed source-load BCT model has obvious advantages in carbon mitigation. [ABSTRACT FROM AUTHOR]

The decarbonization of the power system forces the rapid development of electric energy storage (EES). Electricity consumption is the fundamental driving force of carbon emissions in the power system. However, the current EES capacity planning research that considers the load-side carbon emission responsibility is still limited. To fill this research gap, this paper proposes a carbon incentive mechanism while considering load-side carbon emission responsibility. Additionally, a bi-level optimal capacity planning model of the load-side EES based on carbon emission flow (CEF) theory is proposed. The upper level obtained the bus carbon intensities through the optimal economic dispatch and passed them to the lower level. Considering the carbon incentive mechanism, the lower level optimized the EES capacity. Finally, the model was tested by MATLAB/Gurobi in the modified IEEE-39 bus power system. The results show that under the stimulation of the carbon incentive mechanism, the bi-level optimal capacity planning model of the load-side EES could effectively promote peak shaving, valley filling, and carbon reduction. Furthermore, compared with the two existing EES subsidy policies, the proposed carbon incentive mechanism is verified to be more conducive to reducing system carbon emissions. [ABSTRACT FROM AUTHOR]

The development of emerging technologies has enhanced the demand response (DR) capability of conventional loads. To study the effect of DR on the reduction in carbon emissions in an integrated energy system (IES), a two-stage low-carbon economic dispatch model based on the carbon emission flow (CEF) theory was proposed in this study. In the first stage, the energy supply cost was taken as the objective function for economic dispatch, and the actual carbon emissions of each energy hub (EH) were calculated based on the CEF theory. In the second stage, a low-carbon DR optimization was performed with the objective function of the load-side carbon trading cost. Then, based on the modified IEEE 39-bus power system/Belgian 20-node natural gas system, MATLAB/Gurobi was used for the simulation analysis in three scenarios. The results showed that the proposed model could effectively promote the system to reduce the load peak-to-valley difference, enhance the ability to consume wind power, and reduce the carbon emissions and carbon trading cost. Furthermore, as the wind power penetration rate increased from 20% to 80%, the carbon reduction effect basically remained stable. Therefore, with the growth of renewable energy, the proposed model can still effectively reduce carbon emissions. [ABSTRACT FROM AUTHOR]

The stochasticity and volatility of renewable energy have become a major stumbling block to its widespread use. Complementary wind-CSP energy systems (WCES), which are consisted of low-cost wind power and dispatchable concentrating solar power (CSP) with thermal energy storage (TES), are developed to mitigate renewable energy generation instability effectively. Successful implementation of a WCES requires determining two key issues: the installed TES capacity in the system and the energy dispatch strategies in different price modes. This paper proposes a two-stage WCES decision-making framework for coordinating thermal energy storage capacity planning and energy dispatch through multi-channels: wind, solar, and TES. In the first planning stage, we proposed a stochastic model to solve the TES capacity planning problem, which considers the construction cost, the unstable supply of energy resources, and the future production dispatch of WCES. Given the TES capacity determined in the planning stage, another stochastic model is developed in the second operating stage to solve the WCES energy dispatch problem, which determines the real-time energy dispatching by considering the real-time energy supply, demand, and electricity price. The two stochastic models are converted into MILP models using scenario-based and linearization methods. The results show that the proposed models are effective. With further consideration in carbon emission control for the capacity planning, we suggest reserving an additional 20% of the existing optimized capacity for a tighter policy. We show the optimal energy dispatch strategy for the dispatch strategy is to dispatch wind power first, then solar power, and reserve TES energy for power shortage. We also show that the WCES could seize more energy arbitrage opportunities among the three different electricity price models and obtain more benefits in the real-time electricity price model. [ABSTRACT FROM AUTHOR]