Stochastic optimization approaches are achieving growing interest in application to difficult optimization problems common in chemical and process engineering. They are easy to use especially in cases where there are ...
详细信息
Stochastic optimization approaches are achieving growing interest in application to difficult optimization problems common in chemical and process engineering. They are easy to use especially in cases where there are disjunctive functions and logical conditions. M-LI optimization algorithm from adaptive random search (ARS) strategy has been developed for most advantageous optimization problems, i.e. MINLP ones. This method is an extension of LJ algorithm developed by Jaakola and Luus (1974) for problems with continuous variables. The main modification in M-LJ method is the change of distribution function for integer variables. The results of tests are presented and the comparison with the performance of other stochastic methods as e.g. MSIMPSA algorithm from Cardoso et al. (1997). M-LJ has shown high robustness and can be seen easy to use optimization tool for certain MINLP problems. Also, the re-formulation of difficult optimization MINLP problem for batch processes from Kocis and Grossmann (1988) has been developed which allows for solving it as NLP task.
The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, othe...
详细信息
ISBN:
(纸本)9781457710001
The large increase of Distributed Generation (DG) in Power Systems (PS) and specially in distribution networks makes the management of distribution generation resources an increasingly important issue. Beyond DG, other resources such as storage systems and demand response must be managed in order to obtain more efficient and "green" operation of PS. More players, such as aggregators or Virtual Power Players (VPP), that operate these kinds of resources will be appearing. This paper proposes a new methodology to solve the distribution network short term scheduling problem in the Smart Grid context. This methodology is based on a Genetic Algorithms (GA) approach for energy resource scheduling optimization and on PSCAD software to obtain realistic results for power system simulation. The paper includes a case study with 99 distributed generators, 208 loads and 27 storage units. The GA results for the determination of the economic dispatch considering the generation forecast, storage management and load curtailment in each period (one hour) are compared with the ones obtained with a mixed integer non-linear programming (MINLP) approach.
In this paper, a general method for handling disjunctive constraints in a MINLP optimization problem is presented. This method automates the reformulation of an, in a abstract modeling language given, optimization pro...
详细信息
In this paper, a general method for handling disjunctive constraints in a MINLP optimization problem is presented. This method automates the reformulation of an, in a abstract modeling language given, optimization problem into a mathematical problem that is solvable with existing optimization tools. This implementation can use common MILP solvers for linear problems and nonlinear methods for quasi-convex optimization problems. It also includes the possibility to use the logics in the system and solve the system logically using subproblems.
Microgrids are growing at a right pace due to their advantages towards the economical environmental sustainability. Effective operation and management of microgrids can significantly help both the microgrid operator a...
详细信息
ISBN:
(纸本)9781538642924
Microgrids are growing at a right pace due to their advantages towards the economical environmental sustainability. Effective operation and management of microgrids can significantly help both the microgrid operator and customers to get economic and technical benefits. In addition to distributed generation (DG) and battery energy storage system (BESS), a microgrid can effectively utilize demand response (DR) strategy to better manage the energy and improve the performance of the network. In this paper, a mathematical formulation for day-ahead energy management of a microgrid is developed. The day-ahead scheduling of resources have been done with the available information of DG, load demand and electricity price. The simulation case studies with two DR schemes have been carried out for four bus test system and results are presented to compare the DR schemes.
暂无评论