Power system planning with multiple distributed generators

Abstract

Due to increasing energy demand and increasing interest in green energy, the conventional energy generation, transmission and distribution system is now in the changing phase. Energy generation is now preferred from renewable resources. To reduce the burden of transmission lines and due to the increasing energy demand, generation is preferred near the distribution end. Due to all this, the need of Distributed Generation (DG) and its proper planning is highly needed. There have been many studies showing the penetration of DG sets in some standard test systems but still there is a major concern about finding an optimum location for practical system which is far bigger than the test systems shown in the research. Current studies need simulations and many runs of load flow for placing one DG in small systems. If such an approach is applied for larger systems, it will result in large simulation time and high computation requirements i.e., scalability becomes doubtful. Such constraints become obvious and observable considerably when the system needs placement of multiple DG sets to cope with the increasing electricity demands. Hence, a systematic principle to address the issue of optimal DG placement is highly needed. The method presented in this study tries to initially reduce the search space in the given system by selecting optimum locations w.r.t stated objectives, and then finds the optimum sizes of DGs simultaneously, hence reducing both the simulation time and computation requirements. Along with increasing the scalability in this way, the resulting solution also ensures the loss minimization not only for single load level but for entire desired period. Reduced line flows/ loadings and MVA intake from main grid, increased short circuit level of the system buses, and improved bus loading capabilities are the other ancillary services that optimal DG placement can also provide. Hence, another important finding of this work is that it proves the necessity of optimal DG placement which, in turns, ensures maximum financial and energy benefits for system planners and Distributed Network Operators (DNOs). To make the solution practically implementable, geographical considerations are also incorporated. Considering such design parameters helps in finding a practically implementable optimum which is also electrically suitable. It is hoped that the system will be flexible in the sense that if in future some other parameters, constraints and/or objectives needs to be added, it will work with them.