Scope of Intelligence Approcahes for Unit Commitment Under Uncertain Sustainable Energy Environment For Effective Vehicle To Grid Operations-A Comprehensive Review

. Electric vehicles are getting popularity as these are eco-friendly and could be a part of power sector in the future. Electric Vehicles are the smart hybrid vehicles, which stores electric power during their operation, which could be stored in storage cells. These electric vehicles may be plug-in electric vehicles or battery operated electric vehicles. The concept of aggregators may be utilized, wherein the stored energy in vehicles could be supplied to grid during parking hours .This also facilitate the consumers to sale power during the high power demand and purchase power during low power demand. Thus, a bi-directional flow of power could be possible either from vehicle to grid or vice-versa. A large penetration of electric vehicles could result in increase in power demand which could be compensated by proper coordinated unit commitment and optimization techniques. The increasing load on grid by the impact of demand and trends in small generating units which require proper selection of number of generating units to put in line and other units in off condition calls for the concept of unit commitment. It is the selection of more efficient units to be in service and shutting down the other unit while maintaining all the other constraint constant. This would result in effective power flow in an economic manner, simultaneously maintaining the adequacy and reliability of the system. The proposed research represents the scope of intelligence algorithm for unit commitment problem with effective solution of vehicle to grid operations along with sustainable energy for realistic power system.


INTRODUCTION
In the era of development and advancement in technology, electrical power plays a vital role as power is essential commodity for the functioning of any industrial growth. As the main source of electrical energy are fossil fuel energy sources which are diminishing rapidly and would lead to an end one day. This requires careful attention to make systematically and economically the use of these resources. Also ,the power generation companies needs to motivate the small power industries to motivate the small power generation units to take part in the contribution to take part in the contribution to decrease the threads of power crisis. As a necessity one should make enough attempts to utilize non-conventional energy sources so that the burden on conventional energy source could be reduced to a larger extend. This would result in more and more involvement of sustainable energy sources. But as we know that these sources are intermittent energy sources, as they do not provide a constant output power. Transforming present energy system towards one emphasized by renewable energy comes with some challenges, leads to make proper co-ordinate use of conventional and sustainable energy sources provides the optimum power generation and transmission could be possible maintaining all the constraints constant. Secondly, the invent of hybrid electric vehicles could also play an important role to reduce the dependency on conventional energy source. Thus, the proposed research proposal is the development of a hybrid system which combines the overall features of unit commitment, renewable energy sources with the effect of hybrid electric vehicles.

LITERATURE REVIEW
Electrical power is generated by various power generating units such as thermal, hydro, nuclear, solar and wind etc. These generating units are required to turn on and off in proper sequence, while doing so the expense of on off/ should be minimum. This process of analysis and continuously changing generating schedule is referred to as unit commitment (UC) [1].Unit commitment is an optimization problem used to select the operation schedule at each hour interval with changing loads within permissible limit. Also due to large penetration of intermittent sources led to more complex in satisfying the supply and demand. Esmaeeli et al. [2] in his work suggested that with increase in wind power, power generation flexibility must increase as well. Jonghe et al. [3] describes the various complexities introduced in generation schedule by these intermittent sources . Shahriar et al. [4] presents a new technique to solve problems due to renewable energy penetration by formulating a Mixed Integer Linear Programming (MILP) technique. Further, Wang et al. [5] presents the increased complexity due to introduction of large electric vehicles as new participants in the existing power system. However, Vehicle to grid (V2G) can help to improve the reliability and stability of the grid, alleviate power shortages, and reduce peak power, spinning reserve, voltage and frequency regulation .Yang et al. [6] proposed a hybrid meta-heuristic method for solving mixed integer unit commitment problem integrating with significant plug-in electric vehicles. These electric vehicles can act as storage cells when large number of batteries are connected to form aggregators. These aggregators may be used as spinning reserve thus reducing dependency on conventional generating sources. The combined activity of the hybrid power system should be utilized appreciably in intellectual manner to effectively produce economic generation schedule. This calls up for new novel algorithms for providing more optimal solutions to these hybrid UC problems .Dynamic programming (DP) [7], Lagrangian relaxation(LR) [8],Harmony search (HS) [9], Particle swarm optimization (PSO) [10],Genetic algorithm(GA) [11] are some of the recent optimization methods to handle conventional UC problem. Seth et al. [12] in his work describes improved priority list method to effectively a ramp rate constrained unit commitment. Table-1 presents a comprehensive review of some related intelligent and innovative approach to tackle complex UC problems.

MATHEMATICAL MODELING
The objective of unit commitment is to plan the optimal schedule of available generating units to minimize the total operational and generation cost. Total cost of power generation includes fuel cost, shut down and start-up costs within constraints limit.
The various constriants are as follows:

Operating Cost
The operating cost of i th at h th hour and can be represented as below: Where, Fhi is the cost associated with the i th generating unit at h th hour and i a , i b and i c are its fuel and operational cost coefficients, Uhi and U (h-1)i is the committed status of the ith unit at h th hour and (h-1)-th hour respectively, SUChi is the start-up cost of i th unit at h th hour Combined cost (Fh), for all the generating units (NG) at a particular hour 'h' can be obtained as the sum total of all the individual units 'costs: Now, the total fuel cost F is the double summation of the costs incurred for all the generators for all the time periods considered. It can be mathematically represented as:  is duration for which the i-th thermal unit has been continuously off until hour h. i CSH is the cold start hour of i-th unit. The start-up cost for a unit depends on its downtime. If it is longer than the related MDt plus its predefined Cold-Start Hours (CsH), Cold-Start Cost (CSc) is needed to operate it. Else if the ith unit down time is shorter than the mentioned duration, Hot-Start cost (HSC) is needed to operate it. The Various Constraints linked with unit commitment problem are explained below.

Maximum (max) and Minimum (min) Operating Limits of Generators
Every unit has its own maximum/minimum power level of generation, beyond and below which it cannot generate.

Load Balance Constraints
The load balance or system power balance constraint requires that the sum of generation of all the committed units at hth hour must be greater than or equal to the demand Dh at a particular hour 'h'. The power generation of the NG generating units at a particular time horizon handles theload balance constraint and other operating limit constraints. To satisfy the equality constraints, one unit is designated as reference unit and its power generation is decided as follows: For any arbitrarily available unit output power generation( Phi), (

Crew Constraints
If a plant consists of two or more units, there may not be enough crewmembers to attend all the units simultaneously while starting up.

Initial Operating Status of Generating Units
Its decides minimum up /down time satisfaction of every unit depending upon data of last day's previous

Constraints Repairing Mechanism
The flowchart for constraint handling mechanism during charging and discharging phase has been depicted below. The Fig.1 shows the constraint handling mechanism for UCP minimum up and down time. And Fig.2

SCOPE OF RESEARCH
The analysis of present review suggest that UC problem is aimed at determining the turn-On and turn -off schedules of thermal units to meet forecasted demand for a certain time interval and belongs to a combinatorial optimization problem. Further, it is clear that optimization falls roughly into three categories: heuristic search, mathematical programing, and hybrid methods. There are several optimization strategies employed to solve the complexity of generation scheduling and dispatch problem. Some of these methods are the, Bat Algorithm, Binary  From the research paper listed above in Table-1 and research problem, it have been seen that great efforts are taken for achieving economic load dispatch and unit commitment problem using different methodologies, but there needs some more considerable efforts are made to find solution for global optimized solution (within local and global search space) for cost effective solution for unit commitment problem for vehicle to grid operation in sustainable energy environment, which seriously affects the optimality of the results. Also, it is clear from the literatures, appreciable efforts are made to solve the unit commitment problem using various meta-heuristics optimization methodologies, but no significant efforts are made to find out the global optimization search algorithm by combining local and global search capability of algorithm to get more improved results. Further, research theorem has logically suggested that none of the optimization algorithm is able to provide exact solution to all types of optimization problems efficiently. In other words, there is always scope of improvements to upgrade current techniques to better solve maximum optimization problems efficiently.
In recent research studies pertaining to optimization algorithm, it has been reported that swarm intelligence optimization have some drawbacks and need advanced solutions. Another important concern in swarm intelligent algorithm is regarding exploration, exploitation and convergence. In practical application, it creates serious problem. This motivated our attempts to propose yet another memetic solution of with due consideration of sustainable energy sources along with vehicle to grid concept.

Conclusion And Future Scope
In the proposed research, the authors has successfully presented the scope of solution intelligence solution strategies for effective vehicle to grid operation for unit commitment problem with due consideration of sustainable energy sources. Further, Electric vehicles are getting popularity due to eco-friendly nature and could be a part of power sector in the future. Thus, proposed research explores the concept of modern smart grid system along with effective solution strategies. It is recommended that, in order to explore the scope of intelligence approaches in future learning, the following future work may be taken into consideration:  Consideration of memetic intelligence approaches for cost effective solution of unit-commitment due consideration of uncertainty of sustainable power and vehicle to grid operations.
 Development of hybrid optimization algorithm by combing local search algorithm with modern global search algorithm for constrained optimization problem can be explored and its performance testing can be made on various standard unimodal, multi model, fixed dimension and engineering benchmark problems.
 Simulation environment can be created for implementation of unit commitment problem of electric power system with due consideration of Vehicle to Grid operation in stochastic sustainable environment energy environment  Testing and validation of proposed memetic intelligence algorithm can be explored for various IEEE benchmarks problems with incorporation of V2G operations in stochastic sustainable environment.