Stochastic optimal planning scheme of a zero-carbon multi-energy system (ZC-MES) considering the uncertainties of individual energy demand and renewable resources: An integrated chance-constrained and decomposition algorithm (CC-DA) approach

Abstract

Renewable resources stochasticity and energy demand variation are inevitable during the operation of energy systems. Thus, this paper presents novel stochastic planning and operation of a zero-carbon multi-energy system (ZC-MES) taking the uncertainties of individual energy demand and environmental conditions into consideration. The comprehensive mathematical model is developed as an optimization problem using Monte Carlo scenario generation, fast forward scenario reduction approach, chance-constrained programming, duality theory, and big-M linearization approach. Furthermore, benders decomposition is applied to split the large-scale optimization problem into an investment master problem (MP) and operation subproblem (SP), which are then solved iteratively. The obtained results indicate that by considering all the energy demand uncertainties as an individual entity in the model, the selected capacities of PV, AC, and HWS increase by 60%, 21%, and 10.6%, respectively, while, WT, WSHP, BES, and EC reduce by 14%, 15%, 11%, and 1.09%, respectively. Also, the optimal operation cost for the proposed model reduced by 5–10% compared to other scenarios, however, its annualized investment cost is 3–12% higher but the overall economic implication is optimal. In summary, the underlying reason for this optimal result is the combination of energy storage temporal arbitrage and energy output shifting technique that was implemented by the algorithm to maintain an optimal interrelationship balance and to favour the optimal sizing of the chosen technologies.