Microgrids: Planning of fuel energy management by strategic deployment of CHP-based DERs  An evolutionary algorithm approach
Planning of fuel energy management of a CHP-based microgrid requires strategic deployment of DERs. Strategic deployment of DERs is meant to select their optimal locations, optimal sizes and optimal technologies. Optimal locations and sizes, which are independent of types of CHP-based DERs used, are selected, here, by loss sensitivity index (LSI) and by loss minimization using differential evolution (DE) algorithm respectively. In the context of planning of a 14-bus radial microgrid present paper incorporates originality in ideas in the analysis technique based on differential evolution (DE) algorithm to evaluate how different optimal output sets of a group of four DERs, while operating within their respective capacity limits as well as tracking an electrical demand without grid participation, could satisfy a range of heat demands, each representing a specific solution of optimal fuel consumption. For investment decision in the perspective of the owner of the microgrid the author performs the analysis with two separate groups of 4-DER each of various sizes  one group with all diesel generators (i.e. All-Dg) and other with mix of diesel generators (Dgs) and microturbines (Mts) (i.e. Mix-DER). As the present microgrid is intended to cater both heat and electric demands, as per profiles, to its all-commercial customers, a boiler is considered as need-based back-up source to meet the deficit of daily heat generation, if any, for balancing the daily heat demand. At the best result of optimal fuel cost of each group obtained from above analysis, an economic comparison is done between the two groups with the help of pay back period (PP), internal rate of return (IRR) and net present value (NPV) of the microgrid to decide better investment option of the two. Results of DE are confirmed and compared with particle swarm optimization (PSO) technique.
microgrid differential evolution diesel generator microturbine particle swarm optimization