Mahdi Motalleb

Frequency regulation in Islanded microgrid using demand response

Introducing more Distributed Generation (DG) into power grid infrastructure drives more attention to understand how large scale DG affects grid operation. Islanding is an important concern in this area. Islanding refers to the condition that DGs within a microgrid continue energizing while the microgrid has been disconnected from the main grid. Considering the adverse effects of Islanding, it should be detected and managed in a proper way. After Islanding has been detected, even though the first option is tripping all DG units and shutting down the system, the microgrid has this option to work as a standalone system. For this, the microgrid frequency regulation is a must. This paper comprises two parts. First proposing an islanding detection method based on communication between the microgrid and the main grid. In the second part, after detectiong the islanding using the first part, it presents an effective implementation Demand Response (DR) to regulate the frequency in islanded microgrid as an Ancillary Service (AS) considering the transient constraints of frequency in inverter-based generations.

Demo abstract: testbed for distributed demand response devices—internet of things

As the drive to integrate renewable resources into the modern power grid grows, services such as demand response (DR) capable of providing ancillary services (AS) for grid stabilization has become a central focus of current research. While DR programs are effective in theory, utilities are reticent to implement because of lack of data. The next step is to provide a platform to test proposed mechanisms to move toward implementation. This work presents a complete package for internet-of-things hardware-in-the-loop simulation offering scalability in simulation, integration with real and simulated device nodes and full control over grid parameters. It is a DR testbed providing telemetry and actuation for connected loads which, itself, serves as a node in a larger software based PSIM power simulation. Simulated node’s characteristics are modeled from the real node’s data. Increasing penetration of intermittent renewable generation resource challenges grid operators in providing stability to the grid and balancing generation to meet load [1–3]. In addition to improving social welfare [4] DR has been shown to be a viable solution to providing a portion of AS which is able to contribute to stabilizing grid conditions [5–7]. The testbed is used to test a DR response algorithm published previous at the REDlab for grid frequency control using DR [8].

Poster abstract: demand response market considering dynamic pricing

In this work, a power market framework is presented comprised of demand response aggregators (DRAs) trading energy stored in residential batteries. Competition is modeled by a Stackelberg game, with market clearing hourly, which determines each DRAs bidding strategy that will maximize payoff. The magnitude and price of power transactions allowable is controlled by the game’s leader. Dynamic pricing is considered in two forms. As demand updates, dynamic economic dispatch is used to update utility generator dispatch. The marginal electrical price offered by the utility updates each time interval reflecting updated supply and demand (real time pricing). Time-of-use load scheduling through dynamic programing combined with dynamic pricing optimizes load scheduling and reduces total demand in the system. The method schedules utility generation minimizing supply-side operational cost, and in turn, demand-side electricity cost at all times (valley, peak, off-peak) and is thus mutually advantageous.

Reliability assessment of distribution system using fuzzy logic for modelling of transformer and line uncertainties

Reliability assessment of distribution system, based on historical data and probabilistic methods, leads to an unreliable estimation of reliability indices since the data for the distribution components are usually inaccurate or unavailable. Fuzzy logic is an efficient method to deal with the uncertainty in reliability inputs. In this paper, the ENS index along with other commonly used indices in reliability assessment are evaluated for the distribution system using fuzzy logic. Accordingly, the influential variables on the failure rate and outage duration time of the distribution components, which are natural or human-made, are explained using proposed fuzzy membership functions. The reliability indices are calculated and compared for different cases of the system operations by simulation on the IEEE RBTS Bus 2. The results of simulation show how utilities can significantly improve the reliability of their distribution system by considering the risk of the influential variables.

Internet-of-Things Hardware-in-the-Loop Simulation Architecture for Providing Frequency Regulation with Demand Response

Following recent advances in network infrastructure, cloud computing, and embedded systems, fascinating work is underway exploring the utility of demand response in increasing grid stability while permitting high penetration of intermittent renewable distributed generation resources. Although works have demonstrated diverse theoretical advantages of demand response programs, little real-world data are available and utilities generally remain reticent in moving forward with large-scale implementation due to risks inherent to any modification of the power system. Deciding that the next pertinent step in bringing demand response from theory to technology is developing an Internet-of-things hardware-in-the-loop simulation power system integrated device capable of empirically testing the theoretical mechanisms, this work presents an architecture testbed for providing demand response (telemetric monitoring and actuation of loads), which is real node in a power system simulation where virtual nodes parameters derive real node data. We test a demand response algorithm, which provides frequency regulation services.