A New Pricing Scheme for Controlling Energy Storage Devices in Future Smart Grid

Abstract

Improvement of the overall efficiency of energy infrastructure is one of the main anticipated benefits of the deployment of smart grid technology. Advancement in energy storage technology and two-way communication in the electric network are indispensable components to achieve such a vision, while efficient pricing schemes and appropriate storage management are also essential. In this paper, we propose a universal pricing scheme which permits one to indirectly control the energy storage devices in the grid to achieve a more desirable aggregate demand profile that meets a particular target of the grid operator such as energy generation cost minimization and carbon emission reduction. Such a pricing scheme can potentially be applied to control the behavior of energy storage devices installed for integration of intermittent renewable energy sources that have permission to grid connection and will have broader applications as an increasing number of novel and low-cost energy storage technologies emerge.

Hardware‐in‐the‐loop demonstration of automated demand response for distribution networks using PMU and MQTT

“…time of‐day, day‐of‐week). Some of the control techniques proposed for DR management are; direct load control [10], dynamic demand control [11], and real‐time pricing control [12]. Traditionally, DR is focused on the curtailment of some electrical loads during peaking periods.…”

Demand Response Load Following of Source and Load Systems

“…As for the demand response load agent LA1, there are 10 TCL aggregators with a total number of 14667 cooling air-conditioners. The width of all temperature deadbands is δ db = 1 • C; the thermal resistance R of each load aggregator is chosen from [4.5, 5.5] uniformly; the thermal capacitance C of each load aggregator is chosen from [8,12] uniformly; the output cooling energy P ra of each load aggregator is chosen from [16,20] uniformly; the coefficient of performance η of each load aggregator is chosen from [2.6, 3] uniformly. The preferred setpoint for each aggregator is given with θ des set,i = [23.15, .…”

“…On the other hand, for the demand response load agent LA2, there are 16 TCL aggregators with a total number of 19997 cooling air-conditioners. Suppose the width of the temperature deadband δ db = 1 • C; and R ∈ [1.5, 2.5], C ∈ [8,12], P ra ∈ [12,16], η ∈ [2. 6,3] uniformly.…”

“…Real-time wind power injection to the system P max D EMAND response (DR) in smart grids [1] has been proposed and investigated for several decades [2], [3], [4], which can be served as a utility manager to redistribute the electricity demand for a certain period of time, e.g., timeof-day, day-of-week. Several control techniques have been proposed for DR management, such as, direct load control [5], dynamic demand control [6], real-time pricing control [7], [8]. However, the basic problem for DR is how to make efficient energy consumption schedules for massive amounts of smart loads.…”