An Exploratory Analysis of the Market Perspective on Reclaiming Chromated Copper Arsenate (CCA) from Decommissioned Preservative-Treated Wood Utility Poles

In the area of recycling of spent chromated copper arsenate (CCA)-treated wood, most studies to date have focused on methods of removing/extracting the residual preservative from the wood matrix. It is well recognized that exposure of CCA-treated wood to an acid solution can reverse the CCA fixation process thereby converting the CCA elements into their water-soluble form. The economic viability of the process is enhanced because it can be integrated with other technologies and products (e.g., “green” spray foam insulation, etc.). The market for the “green” CCA is the same as for traditional CCA-the wood treating industry, principally utility poles and pilings. A market research study was conducted to determine the suitability of spent CCA-treated wood as a source for recycled, “green” CCA for manufacturing “green” spray-foam insulation. Specifically, we wanted to discern the attitudes and overall perspectives of buyers/sellers (i.e., utilities and wood treating companies) of CCA preservatives and treated wood products, disposal methods and costs for decommissioned CCA-treated wood, and understand perceptions of and willingness-to-pay for “green” CCA preservatives extracted from the technologies used in this research. Results show that 60% of wood preservative treating respondents and 60% of electric utility company respondents are somewhat or greatly interested in using out-of-service utility poles as feedstock for “green insulation” as part of a new potential business venture.

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.