{"title":"Dynamic Network Energy Management via Proximal Message Passing","authors":"Matt Kraning, E. Chu, J. Lavaei, Stephen P. Boyd","doi":"10.1561/2400000002","DOIUrl":"https://doi.org/10.1561/2400000002","url":null,"abstract":"We consider a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its owndynamic constraints and objective, connected by AC and DC lines. The problem is to minimize the total network objective subject tothe device and line constraints, over a given time horizon. This is a large optimization problem, with variables for consumptionor generation for each device, power flow for each line, and voltage phase angles at AC buses, in each time period. In this paperwe develop a decentralized method for solving this problem called proximal message passing. The method is iterative: At each step,each device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing itsown objective function, augmented by a term determined by the messages it has received. We show that this message passing methodconverges to a solution when the device objective and constraints are convex. The method is completely decentralized, and needs noglobal coordination other than synchronizing iterations; the problems to be solved by each device can typically be solved extremelyefficiently and in parallel. The method is fast enough that even a serial implementation can solve substantial problems inreasonable time frames. We report results for several numerical experiments, demonstrating the method's speed and scaling,including the solution of a problem instance with over 10 million variables in under 50 minutes for a serial implementation;with decentralized computing, the solve time would be less than one second.","PeriodicalId":329329,"journal":{"name":"Found. Trends Optim.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128989302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information Relaxations and Duality in Stochastic Dynamic Programs","authors":"David B. Brown, James E. Smith, Peng Sun","doi":"10.1287/opre.1090.0796","DOIUrl":"https://doi.org/10.1287/opre.1090.0796","url":null,"abstract":"We describe a general technique for determining upper bounds on maximal values (or lower bounds on minimal costs) in stochastic dynamic programs. In this approach, we relax the nonanticipativity constraints that require decisions to depend only on the information available at the time a decision is made and impose a “penalty” that punishes violations of nonanticipativity. In applications, the hope is that this relaxed version of the problem will be simpler to solve than the original dynamic program. The upper bounds provided by this dual approach complement lower bounds on values that may be found by simulating with heuristic policies. We describe the theory underlying this dual approach and establish weak duality, strong duality, and complementary slackness results that are analogous to the duality results of linear programming. We also study properties of good penalties. Finally, we demonstrate the use of this dual approach in an adaptive inventory control problem with an unknown and changing demand distribution and in valuing options with stochastic volatilities and interest rates. These are complex problems of significant practical interest that are quite difficult to solve to optimality. In these examples, our dual approach requires relatively little additional computation and leads to tight bounds on the optimal values.","PeriodicalId":329329,"journal":{"name":"Found. Trends Optim.","volume":"1037 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131481604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}