2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 Paper Identification Number - 86 978-1-4244-4331-4/09/$25.00© 2009 IEEE 1 Hybrid Energy System Sizing Incorporating Battery Storage: An Analysis via Simulation Calculation Ajai Gupta*, R P Saini, and M P Sharma Alternate Hydro Energy Centre Indian Institute of Technology - Roorkee Roorkee – 247667, Uttarakhand, India *Email: ajai_abjc@yahoo.com Abstract—In practical decentralized hybrid energy systems, there are often different renewable/conventional generators and battery storage. An over sizing of the system components significantly elevates the overall cost of the hybrid energy system. In this context the correct and cost effective system sizing as well as efficient system operation is important. In order to determine the optimal sizing of system components, a mixed integer linear mathematical programming model (time-series) has been developed, based on the evaluation of optimized system unit cost for a hybrid energy generation system consisting of small/micro hydro, biogas, biomass, photovoltaic array, a battery bank and a fossil fuel generator. An optimum control algorithm written in C++, based on combined dispatch strategy, allowing easy handling of the models and data of energy system components is presented. The sizing result of the components is based on a trade-off between the optimized cost of the system and other techno-economics parameters, as determined by the algorithm in conjunction with a time-series model. To demonstrate the use of model and algorithm, an application example is also presented. Keywords-hybrid energy system; combined dispatch strategy; integer programming; hybrid system sizing I. INTRODUCTION A promising solution to electrify the isolated locations far from the electrical distribution network is the application of the stand-alone Hybrid Energy System (HES), which combines renewable/conventional sources with battery. Successful implementation of this technology depends largely on its optimal design. An important aspect of this design is sizing. Sizing means calculating the size of the different components required to supply the loads during the worst climatic conditions at minimum cost. Generally, there are three main approaches [1] to achieve the optimal configurations of hybrid systems in terms of technical and economical analysis, i.e. the least square method, the loss of power supply probability method, and trade-off method [2]. In [2], a mathematical model has been introduced to determine the optimal configuration of the hybrid energy system while satisfying system operational constraints. Model is solved by HyperLindo software without using any dispatch strategy. In [3], a model has been introduced to estimate the optimized cost and solved by dispatch strategy based algorithm but does not based on any cost-effective optimization approach. In this paper, model design approach in [2-3] and solution approach [3] has been extended to include the cost effective ap- proach with hourly energy balance concept while introducing generalize mix-integer linear mathematical programming model (time-series) and combined dispatch strategy based solution algorithm, based on the evaluation of optimized system unit cost, to determine the optimal operation, optimal sizing including the assessment of the economic penetration levels of photovoltaic array area for a hybrid energy system. The sizing result of the components is based on a trade-off between the optimized cost of the system and other techno- economics parameters, as determined by the algorithm in conjunction with a time-series model. A cost effective approach of the proposed model is that a cost constant (cost/unit) for each of the resource is introduced in the cost optimization function in such a manner that resources with lesser unit cost share the greater of the total energy demand in an attempt to optimize the function. II. HYBRID ENERGY SYSTEM CONFIGURATION The block diagram for a typical stand-alone hybrid energy system, based on a generalized three-bus configuration is shown in Fig. 1. The system consists of micro-hydro generator (MHG), biogas generator (BGG), biomass (fuelwood) generator (BMG), photovoltaic generator (PVG), battery bank (BATT), back up diesel generator (DEG), and dump load. Provisions for the availability of both AC and DC buses are made using electronic converters. To serve the load, electrical energy can be produced either directly from renewable generators and diesel generator, or indirectly from the battery bank. These relationships are expressed in eq. 1.1 through 1.5. E PVG (t) = E PVG, Load (t) + E PVG, BATT (t) + E PVG, Dump (t) 1.1 E MHG (t) = E MHG, Load (t) + E MHG, BATT (t) + E MHG, Dump (t) 1.2 E BGG (t) = E BGG, Load (t) + E BGG, BATT (t) + E BGG, Dump (t) 1.3 E BMG (t) = E BMG, Load (t) + E BMG, BATT (t) + E BMG, Dump (t) 1.4 E DEG (t) = E DEG, Load (t) + E DEG, BATT (t) + E DEG, Dump (t) 1.5 In any hour t, the energy available to charge the battery and from the battery to serve the load is shown in eq. 1.6 and 1.7 respectively. Finally, the total energy available to serve the load is given in eq. 1.8. E BATT, IN (t) = η CC × η CHG [E PVG, BATT (t) + η REC × (E MHG, BATT (t) + E BGG, BATT (t) + E BMG, BATT (t) + E DEG, BATT (t))] 1.6 E BATT, Load (t) = η DCHG [E BATT, IN (t)] 1.7 E Load (t) = [E MHG, Load (t) + E BGG, Load (t) + E BMG, Load (t) + E DEG, load (t) + η INV (E PVG, Load (t) + E BATT, Load (t))] 1.8 Indian Institute of Technology Kharagpur, INDIA