IoT-Enabled Real-Time Management of Smart Grids With Demand Response Aggregators

Abstract

Integration of widely distributed small-scale Renewable Energy Sources like rooftop Photovoltaic panels and emerging loads like plug-in Electric Vehicles would cause more volatility in total net demand of distribution networks. Utility-owned storage units and control devices like tap changers and capacitors may not be sufficient to manage the system in real-time. Exploitation of available flexibility in demand side through aggregators is a new measure that distribution system operators are interested in. In this paper, we present a developed real-time management schema based on Internet of Things solutions which facilitate interactions between system operators and aggregators for ancillary services like power balance at primary substation or voltage regulation at secondary substations. Two algorithms for power balance and voltage regulation are developed based on modified Optimal Power Flow and voltage sensitivity matrix, respectively. To demonstrate the applicability of the schema, we set-up a real-time simulationbased test bed and realised the performance of this approach in a real-like environment using real data of a network with residential buildings.

Economic Model for Coordinating Large-Scale Energy Storage Power Plant With Demand Response Management Options in Smart Grid Energy Management

“…is the energy level of the energy storage plant and 𝜂 is the energy storage plant efficiency. Constraints (3) and ( 4) indicate the limitation of charge and discharge power of the energy storage plant, respectively.…”

“…This figure proves that the proposed model works well to solve the energy storage plant management problem. Figure (3) shows the energy storage plant's energy status in different cases. According to this figure, it is clear that during nonpeak hours the energy storage plant is charged, and the energy level is higher.…”

“…Figure (6) shows the charge and discharge status of energy storage power plants in the 874-bus system. In general, Tables (3) and ( 4) show the importance of modeled constraints in coordinating the two problems of demand response management and charge and discharge management of storage power plants in the two networks of 33 and 874-bus. It turned out that the combination of these two problems could increase net profit, and also the effect of applying various constraints on the problem of optimal energy storage plant charge and discharge management and demand response management in the objective function of the problem was shown.…”

“…Independent system operators (ISO) can use the flexibility of demand response management programs to maintain reliability cost-effectively [2]. Utilizing demandside flexibility through aggregators is a new move that distribution system operators are interested in, leading to power balance in the primary substation or voltage regulation in the secondary substations [3]. In this paper, the demand side management program is used to increase productivity in smart grids and reduce economic costs along with distributed generation resources and large energy storage systems.…”

“…Figures(7) and (8) also show the changed substation loads per hour after applying each load change value in the 33 and 874-bus network, respectively.Finally, Tables(3) and (4) showed the impact and performance of the proposed model on the revenue from the operation of a battery unit along with the demand response program. In other words, it was shown that the change in the number of charge and discharge times, the problem of demand response, and also the change in the number of hours allowed for the demand response program will affect the profitability of the network.…”