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A micro-grid (MG) comprises different energy sources of different operational characteristics. In this paper, we present an operation management model of a MG integrated with small renewable energy resources. The MG is operating in gridconnected mode and feeding energy to a residential community. The consumers of the residential community are participating in an incentive-based demand response (DR) program. The problem is formulated for the generation scheduling of the MG sources so that the operational cost and pollutants emission from the MG could be minimized in the presence of incentive responsive loads. The problem is then solved by the mixed integer linear programing technique using CPLEX solver of the GAMS software. The simulation results are achieved by solving the problem under three different cases. For the comparison of the results, we consider two scenarios in each case; (i) without DR program, (ii) with DR program. Finally, the trade-off between two conflicting objectives has been analyzed and optimal solution is achieved and presented in the paper.

Demand response (DR) programs have become powerful tools of the smart grids, which provide opportunities for the end-use consumers to participate actively in the energy management programs. This paper investigates impact of different DR strategies in a home-energy management system having consumer with regular load, electric vehicle (EV) and battery-energy storage system (BESS) in the home. The EV is considered as a special type of load, which can also work as an electricity generation source by discharging the power in vehicle-to-home mode during high price time. BESS and a small renewable energy source in form of rooftop photovoltaic panels give a significant contribution in the energy management of the system. As the main contribution to the literature, a mixed integer linear programming based model of home energy management system is formulated to minimize the daily cost of electricity consumption under the effect of different DR programs; such as real time price based DR program, incentive based DR program and peak power limiting DR program. Finally, total electricity prices are analysed in the case studies by including different preferences of the household consumer under mentioned DR programs. A total of 26.93 % electricity cost reduction is noticed with respect to base case, without peak limiting DR and 19.93 % electricity cost reduction is noticed with respect to base case, with peak limiting DR.

Energy management of residential consumers plays a very important role in the achievement of aim of energy saving, lesser cost of electricity for the residential consumers and highest possible satisfaction of the consumers. In this paper, a common energy scheduler for the multiple users has been designed for energy scheduling of residential consumers having smart appliances. Mathematical modelling of different types of appliances has been done to achieve the required results. Time of use tariff and day ahead real time pricing tariff have been used for energy scheduling. A wind energy source has been considered to reduce the burden of energy demand on the grids and to analyse the effect of renewable energy sources in a small scale energy management problem. A delay factor has been also included in the main objective function of the problem to maintain the satisfaction level of the consumers by reducing cost of waiting. The final designed problem is solved by mixed integer linear programming technique using GAMS software. The time taken for the solution is very less. In this paper, to increase the energy efficiency and the accuracy of the required results, the selected time horizon of 24 h is divided into 96 small time slots, each of 15 min duration. Achievement of the aim of the paper, that is reduction in the cost of electricity and attainment of the highest possible satisfaction level of the users, has been verified by the simulation results. Practical application: Our work is completely applicable in the practical field. This work can be used for the energy scheduling of home appliances.