Design and Optimization of Multiple Interconnected Utility Systems in An Integrated Refining and Petrochemical Complex

In an integrated refining and petrochemical complex,a centralized utility system(CUS)is introduced to integrate the steam demands of production plants.Besides,two sub-utility systems(SUSs)located inside the alkene and refinery plants,respectively,can satisfy the shaft demands.It is difficult to determine the steam production of the CUS because the steam demands of the alkene and refinery plants also depend on the design and operation of the SUSs.To explore the complicated interaction between the CUS and SUSs,we proposed a mixed-integer nonlinear programming(MINLP)model for the design and optimization of multiple interconnected utility systems to minimize the total annualized cost(TAC).An extended superstructure was suggested to contain multiple inter-plant connected steam pipe alternatives between the CUS and SUSs.A more accurate model of the complex steam turbine was proposed.Then the proposed MINLP framework is applied to a new integrated refining and petrochemical complex.Two scenarios are investigated in the case study to explore the effect of steam main temperatures on system configurations and operating parameters.By optimizing the main temperatures,a TAC of$2.7 million can be saved.Judging from the results of the two scenarios,the feasibility and effectiveness of the proposed framework for the design and optimization of multiple interconnected utility systems have been demonstrated.

Real-Time Pricing for Smart Grid with Multiple Companies and Multiple Users Using Two-Stage Optimization

In this paper, we focus on the real-time interactions among multiple utility companies and multiple users and formulate real-time pricing(RTP) as a two-stage optimization problem. At the first stage, based on cost function, we propose a continuous supply function bidding mechanism to model the utility companies’ profit maximization problem, by which the analytic expression of electricity price is further derived. At the second stage, considering that individually optimal solution may not be socially optimal, we employ convex optimization with linear constraints to model the price anticipating users’ daily payoff maximum. Substitute the analytic expression of electricity price obtained at the first stage into the optimization problem at the second stage. Using customized proximal point algorithm(C-PPA), the optimization problem at the second stage is solved and electricity price is obtained accordingly. We also prove the existence and uniqueness of the Nash equilibrium in the mentioned twostage optimization and the convergence of C-PPA. In addition, in order to make the algorithm more practical, a statistical approach is used to obtain the function of price only through online information exchange, instead of solving it directly. The proposed approach offers RTP, power production and load scheduling for multiple utility companies and multiple users in smart grid. Statistical approach helps to protect the company’s privacy and avoid the interference of random factors, and C-PPA has an advantage over Lagrangian algorithm because the former need not obtain the objection function of the dual optimization problem by solving an optimization problem with parameters. Simulation results show that the proposed framework can significantly reduce peak time loading and efficiently balance system energy distribution.