A METHODOLOGY FOR DESIGN OF OPTIMAL HYBRID ENERGY SYSTEM SUPPLYING REMOTE AREAS

Abstract

Renewable energy sources along with their non-depleting and non-polluting nature present an attractive source of alternative energy. It has been endeavored worldwide to make it practicable for energy needs. The drawback, however associated with renewable energies is their unpredictable nature. The magnitude of these energies (e.g. wind and solar energy) needed may not actually coincide with the time of demand. Further, independent use of a particular renewable energy system results in an oversized and costly system. For high reliability a suitable mix of several such systems of renewable energies is suggested. The term Hybrid Energy System (HES) refers to a combination of more than one energy conversion device to supply a pool of power. A power backup (e.g. batteries) may additionally be used to supply the energy deficit. The sources used are usually complementary to make a reliable and cost effective power supply. HES would be a viable power supply option for the future. For remote locations where diesel generator is more commonly used for supply, the inclusion of renewable energy based power generators would result in a reduction in the diesel requirement. The HES may sometimes include a battery bank to meet the demand when either peak load demand is high or renewable energy source do not generate enough power. Battery storage also smoothen the mismatch between the occurrence of peak load and maximum power generated at any point of time. The major applications of HES is for remote system applications. They may also be used as a part of a distributed generation application in conventional electricity grid. In both cases an efficient energy management system/control system is required. The problems of stability and power quality are additionally to be handled by such control systems when the system is grid interactive. This is on account of the dynamic interaction between the grid and/ or the loads and the power electronic interface of the renewable energy input systems. The modeling of HES requires a prefeasibility study of the proposed structure for the hybrid system and an appropriate representation of the HES components. The prefeasibility study deals with the study of the potential of energy resources at a particular area where the HES is proposed to be installed. The design of an HES is mainly dependent on the energy requirements in the proposed area. To estimate the overall performance of the HES, individual components should be considered and a mix is to be used for meeting the demand with a high level of reliability. The accuracy in predicting the energy consumption in the area and the appropriateness of energy resource data at the design stage ensures that the HES system operates with optimal performance. Selection of proper sizing of equipment (based on historical weather data and maximum demand capacity) is the next stage of the HES modeling. The reliability and economy of the system is highly dependent on these factors. The constraint in proper sizing of equipment, however, is the availability and cost of a particular system component(s). If the power output prediction from these individual components is precise, then the resultant combination will be able to deliver power within the limits of the system constraints. The criteria, to select an optimum combination of a hybrid system fulfilling the required load demand is usually, Reliability of power supply and/or System economy. Evaluation of HES is usually carried out on the basis of either of the two. The optimal combination gives the best compromise between the available energy resources; their relative cost and other system constraints. Net Present Cost (NPC) and Cost of Energy (COE) are the most frequently used forms of evaluation of system economy. The NPC is defined as the total present value of a time series of cash flows, which includes the initial cost of all the system components, the cost of any component replacements that occur within the project lifetime and the cost of maintenance. Sometimes it is also termed as system life cycle cost. The system lifetime is usually considered to be equivalent to the life of that element of HES which is having the longest lifespan ( usually the photovoltaic module which is used in most HES). The COE is termed as the average cost per kilowatt-hour of useful electric energy produced by the system which can be calculated using annual cost incurred and load served in the year. The basic framework of the research work can be explained as per the following sequence: As a first step, load demand estimation for a proposed remote area and the feasibility of a distribution network supplying power to the local population is to be carried out. It is necessary that the selected area should have sufficient load demand and the energy distribution in the area through a central HES should have a viable scope in relation to the technical and economical issues. For this, the demographic details of the proposed area have to be collected along with the manner in which the energy is to be utilized and also the geographical features of the considered area (especially the terrain and intra-distances) to assess the load demand profile of the area. Assessment of various energy resources in the proposed area such as wind, solar, biomass, hydro energy and diesel etc. is the important second step. A meticulous market search for identification of possible systems combinations for HES will be the subsequent step. The cost of the renewable energy converters along with diesel generator and energy storage devices will be the main factor for the selection of a possible HES configuration. Next, a suitable optimization model has to be applied in order to design an HES scheme with multiple energy resource inputs for the proposed area. An in-depth study of main techniques used in various reported literature is the prerequisite for applying these steps. An improvement in this direction can be achieved by introducing modifications in available techniques and combining them with other methodologies to give a more comprehensive approach for HES design. In the present study the base methodology employs HOMER software (NREL, USA) with additional use of Analytic Hierarchy Process (AHP) and random/Monte Carlo approach. It is felt that such a mixed approach using multi-criteria will give better result than the single criteria approach as implemented in HOMER software. The HES is proposed to be designed in a cluster of remote villages in Uttarakhand state. Uttarakhand is one of the newly created states in India which has been carved out from the state of Uttar Pradesh in the year 2000 AD. This state has a number of hilly regions involving several areas which do not have electricity. The state is presently undergoing development of such areas and electrification is one of the main ways of accomplishing this. However, the remote locations, the difficult hilly terrain, access and small populations make such efforts a challenge. HES installed in different pockets can provide a solution for decentralised power to be made available for local needs and developments. The present study has been directed to the design of a suitable HES for a remote unelectrified village cluster located in Jaunpur block of the Tehri Garhwal district. It is felt that the present work is a contribution to the development of a suitable methodology which will be useful in general for several similar remote and un-electrified pockets in the state. The selection and sizing of possible renewable energy converters and backup sources for the proposed HES has been done with the help of HOMER to obtain an optimized model. Hourly time series simulations for every possible combination have been performed with iii HOMER to obtain a set of feasible systems that satisfies the system constraints. Different combinations of wind turbine and PV array supplemented with diesel generator and battery storage has been investigated to select the most feasible hybrid system to supply power to remote village cluster. The selection of feasible system is based on the NPC of the system. The work introduces an improvement in the existing methodological approach using HOMER in the sense that a better selection of feasible HES are obtained using multicriteria evaluation rather than the single NPC based criteria used in HOMER. For this purpose the author has developed a methodology which involves a mix of HOMER, AHP and random generation. The process involves evaluation of the feasible HES on technical economical and environmental criteria and sub-criteria. In all nine such criteria/subcriteria have been used for which weights have been obtained in terms of AHP. This is used to design a Suitability Index (SI) which assists in ranking of the HES on a multiple criteria basis. There are in all 200 feasible systems obtained with HOMER using the given wind, solar PV, diesel, battery backup and converter systems that have been chosen for the purpose of designing the HES. Suitability indices for these HES are rearranged in order to give the most feasible HES combinations. These feasible HES combinations are further subjected to a robustness check in which the base system values are permitted to vary randomly. The procedure results in a further screening of the existing feasible HES to give the final system which will provide the most suitable performance if implemented in the study area. One thousand random contingency states are generated for each of the nine criteria related parameters which influence the design. A program is developed in MATLAB for generating the random inputs which can be applied to test various HES in relation to the robustness to various contingencies. Based on the above analysis, the most suitable and robust HES that can be recommended for the study area is one having a 2kW solar PV generator, 5 wind turbines (10 kW each ), a diesel generator of 65 kW and 50 batteries (1,156 Ah, 6V each) added with 45 kW converter. The NPC of the system is $ 1,270,483 and the COE is $ 0.296/kWh. A sensitivity analysis on the above system is carried out to understand the scope of the proposed HES. The analysis indicates that how much sensitive the system is, to the changes in sensitivity variables. Recommendation of the study and scope for future work are suggested.