Stochastic optimization for joint energy-reserve dispatch considering uncertain carbon emission

Uncertainty in planned dispatching reserve for day-ahead operations in multi-microgrid distribution networks (MMDN) contributes to the uncertainty of carbon emissions (CEs) from microgrids (MGs). This study proposes a stochastic optimization approach for joint energy-reserve dispatch in MMDN, especially addressing uncertain CE constraints. It points out that the volatilities of renewable energy sources (RESs) and loads and the diversity of reserve sources (RSs), i.e., the subjects provide a reserve, lead to uncertainties in the total reserve requirements (TRRs) and its allocation, affecting the carbon cap margins (CCMs). The volatilities are quantified by employing conditional normal copula theory and fitted to probability density functions (PDFs) of TRRs through enhanced Monte Carlo simulation. The economic and decarbonization aspects of RSs are also evaluated using an improved grey-target model to optimize their shares. Furthermore, a probabilistic model for CCMs is introduced based on the coefficient relationship between the fossil-based RSs' shares and resulting CEs. The method's accuracy and feasibility are validated through simulation results on a modified IEEE 118-node distribution system comprising seven MGs. This study contributes to the advancement of optimization strategies for energy and reserve dispatch in MMDN, particularly addressing uncertainties related to CE constraints and enhancing the economic and environmental performance of the network. •Examining day-ahead joint energy-reserve dispatch for decarbonization and economic optimization.•Utilizing stochastic optimization to address dispatching risks caused by uncertain reserve requirements.•Considering uncertainty in carbon cap margins during day-ahead dispatch.•Facilitating renewable energy consumption and mitigating system operational risks.