Demand response (DR), an integral part of the smart grid, has great potential in handling the challenges of the existing power grid. The potential of different DR programs in the energy management of residential consumers (RCs) and the integration of distributed energy resources (DERs) is an important research topic. A novel distributed approach for energy management of RCs considering the competitive interactions among them is presented in this paper. The impact of participation of RC’s in price-based (PB) and incentive-based (IB) DR programs is investigated using game theory. For this, an energy management optimization problem (EMOP) is formulated to minimize electricity cost. The utility company employs electricity price as a linear function of aggregated load in the PB DR program and an incentive rate in the IBDR program. RCs are categorized into active and passive users. Active users are further distinguished based on the ownership of energy storage devices (SD) and dispatchable generation units (DGU). EMOP is modeled using a non-cooperative game, and the distributed proximal decomposition method is used to obtain the Nash equilibrium of the game. The results of the proposed approach are analyzed using different case studies. The performance of the proposed approach is evaluated in terms of aggregated cost and system load profile. It has been observed that participation in PB and IBDR program benefits both the utility and the consumers.

Demand response (DR) is playing a revolutionary role in changing the way demand at the distribution end is managed. In the literature, a number of centralized energy management schemes have been discussed. Due to large computational overload and privacy concerns in the centralized schemes, distributed schemes are being preferred over centralized schemes. In this paper, an energy scheduling problem (ESP) considering the impact of price-based (PB) and incentive-based (IB) DR programs is presented. The combined effect of PB and IB based DR programs with load-limiting strategy is observed on electricity cost, comfort and system load. In PB DR program, the user is charged according to a quadratic cost function whose coefficients depend on time-of-use pricing. In the IB DR program, an incentive/discount rate is applied to the consumers during peak hours. In PB and IB DR program with a peak limit, the ESP is implemented using a Nash equilibrium problem with pricing. Asynchronous Proximal Decomposition algorithm with shared constraint is implemented to obtain the optimal appliance schedule. In the end, analysis of system load profile, system cost and consumer cost in different cases is performed. The comfort of the consumers is also monitored using a discomfort index. The outcomes of the proposed scheduling scheme are compared with a mixed integer linear programming (MILP) based scheduling scheme proposed in literature. It has been observed that the proposed strategy is useful in reducing load during peak hours and minimizing the electricity bills for residential consumers.

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Increasing power demand, greenhouse gas emissions, and the old infrastructure are serious concerns in the existing power system. With the advent of the smart grid, demand response (DR) has emerged as an effective approach to handle these issues. The selection of an appropriate DR program is vital to acquire the maximum benefits for the utility and the consumers. In this context, a distributed energy management scheme for residential consumers is presented and analyzed to observe the impact of different pricing schemes. The three dynamic pricing schemes considered in this work are based on linear function, the logarithmic function, and the penalty-based linear function of aggregated load. A non-cooperative game is used to formulate the energy management problem of the consumers. The Nash equilibrium of the game is obtained using the proximal decomposition algorithm. The results are obtained for different cases based on the presence of a storage device, a dispatchable generation unit, and two different modes of operation of an electric vehicle. The best pricing scheme is chosen based on the minimum cost, the peak-to-average ratio of the system load profile, and consumer comfort.