{"title":"Efficient Energy Delivery Management for PHEVs","authors":"Mahdi Kefayati, C. Caramanis","doi":"10.1109/SMARTGRID.2010.5621990","DOIUrl":null,"url":null,"abstract":"We consider an Energy Services Company (ESCo) acting as a mediator between the wholesale market and the PHEV owners providing them with energy for battery charging using the distribution network as the delivery infrastructure. Furthermore, the ESCo exploits the flexibility in the charging process by offering reserves in the market. To achieve this objective, the ESCo faces the decision problem of purchasing energy, provision of reserves and scheduling PHEVs for minimization of the expected total cost of operation subject to electricity price volatility and uncertainty, demand deadlines and distribution network capacity constraints. We consider this problem as formulated in [1]. In this paper, we first develop an efficiently computable lower bound on the objective function of this problem. Then, inspired by the lower bound, we propose an efficient linear programming based approximate solution for the problem and analyze its performance under different capacity constraints through simulation. In particular, we demonstrate that for practical ranges of uncertainty, the lower bound is tight and the performance of the proposed solution is very close to the optimal DP solution, effectively eliminating the need for complex solutions. Moreover, we show that with current prices of electricity, this energy delivery management model makes a strong business and reliability case by potentially cutting the PHEV energy costs to less than half, providing substantial amounts of efficient and agile reserve to the grid, counterbalancing the intermittency and uncertainty of the renewable generation, and managing PHEV energy demand to observe distribution network limits.","PeriodicalId":106908,"journal":{"name":"2010 First IEEE International Conference on Smart Grid Communications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"29","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 First IEEE International Conference on Smart Grid Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMARTGRID.2010.5621990","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 29
Abstract
We consider an Energy Services Company (ESCo) acting as a mediator between the wholesale market and the PHEV owners providing them with energy for battery charging using the distribution network as the delivery infrastructure. Furthermore, the ESCo exploits the flexibility in the charging process by offering reserves in the market. To achieve this objective, the ESCo faces the decision problem of purchasing energy, provision of reserves and scheduling PHEVs for minimization of the expected total cost of operation subject to electricity price volatility and uncertainty, demand deadlines and distribution network capacity constraints. We consider this problem as formulated in [1]. In this paper, we first develop an efficiently computable lower bound on the objective function of this problem. Then, inspired by the lower bound, we propose an efficient linear programming based approximate solution for the problem and analyze its performance under different capacity constraints through simulation. In particular, we demonstrate that for practical ranges of uncertainty, the lower bound is tight and the performance of the proposed solution is very close to the optimal DP solution, effectively eliminating the need for complex solutions. Moreover, we show that with current prices of electricity, this energy delivery management model makes a strong business and reliability case by potentially cutting the PHEV energy costs to less than half, providing substantial amounts of efficient and agile reserve to the grid, counterbalancing the intermittency and uncertainty of the renewable generation, and managing PHEV energy demand to observe distribution network limits.