On the power supply side, renewable energy (RE) is an important substitute to traditional energy, the effective utilization of which has become one of the major challenges in risk-constrained power system operations. This paper proposes a risk-based power dispatching strategy considering the demand response (DR) and RE utilization in the stochastic optimal scheduling of parallel manufacturing process (PMP) in industrial manufacturing enterprises (IME). First, the specific production behavior model of PMP is formulated to characterize the flexibility of power demand. Then, a two-step strategic model is proposed to comprehensively quantify multiple factors in the optimal scheduling of DR in PMP loads considering risk-based power system dispatch, thermal generators, wind power integration. Case studies are based on the modified IEEE 24-bus power system, which verify the effectiveness of the proposed strategy in optimally coordinating IME assets with generation resources for promoting the RE utilization, as well as the impacts of power transmission risk on decision performance.

This paper considers a multi-objective resource allocation problem for a multi-agent network where each agent has multiple local objective functions. The goal is to obtain the Pareto optimum by exchanging information between agents. To this end, first, we introduce the weighted L_p preference index to reformulate this problem into a single-objective resource allocation problem, in which the weighting factor of each objective depends on its relative importance. Moreover, in order to reduce the communication burden, we propose distributed event-triggered algorithms to solve the reformulated problem. When local objective functions are strongly convex and have Lipschitz gradients, we prove that the proposed algorithms are free of Zeno behavior and achieve exponential convergence. Finally, we demonstrate the effectiveness of the proposed algorithms by a microgrid network example.