Optimal planning and operation of CHP within micro energy grids
In this study, an optimal planning with respect to capacity sizes and types of prime movers for CHP systems within micro energy grids has been discussed. The objective is to minimize the total net present cost, carbon dioxide (CO2) emission, and mono-nitrogen oxides (NOx) emission for a certain load (electrical or heat) condition. A multi-objective GA (genetic algorithm) was applied to solve the planning problem in order to optimize CHP prime mover types and capacities. Costs, emissions, and efficiencies of CHP prime movers depend on their types, capacity range, and part-load performance. Four candidate CHP prime mover technologies with different characteristics are involved in this study which are; internal combustion engine (ICE), gas turbine (GT), fuel cell (FC), and Stirling engine (SE). The surplus/deficient electricity can possibly be sold to/bought from the main electrical grid, while the remaining heat demand is met from the conventional natural gas based heating units. The approach was applied to four different load type including a typical micro energy grid system as a case study, and the effectiveness of the proposed method was verified. Moreover, a hybrid operational planning algorithm (to maximize primary resource utilization or minimize running cost operation) for CHP prime mover in micro energy grids (MEGs) has been introduced. The proposed operational planning algorithm has been compared with conventional heat and thermal load following modes of operation. The results found the study are very much dependent on the load (heat and electrical) of the system. However, in almost all the scenarios discussed in the study, proposed system with CHP technologies having optimum sizing results is a significant economic and environmental benefit over the conventional energy infrastructure. It is found that, with optimal capacity of PMs, return on investment of the CHP system could be as high as 13% which leads to a payback period of only 7.8 years. Similarly, with the proposed capacity planning tool, maximum achievable CO2 and NOx emission reduction were 15% and 61% respectively. Moreover, from the case studies it is also seen that, proposed hybrid load following modes were consistently able to maximize PM efficiency and minimize system cost during operation.