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S Ashok - One of the best experts on this subject based on the ideXlab platform.

  • peak Load Management in electrolytic process industries
    IEEE Transactions on Power Systems, 2008
    Co-Authors: C A Babu, S Ashok
    Abstract:

    Electrolytic process, employed for manufacturing basic chemicals like caustic soda and chlorine, is highly energy intensive. Due to escalating costs of fossil fuels and capacity addition, the electricity cost has been increasing for the last few decades. Electricity intensive industries find it very difficult to cope up with higher electricity charges particularly with time-of-use (TOU) tariffs implemented by the utilities with the objective of flattening the Load curve. Load Management programs focusing on reduced electricity use at the time of utility's peak demand, by strategic Load shifting, is a viable option for industries to reduce their electricity cost. This paper presents an optimization model and formulation for Load Management for electrolytic process industries. The formulation utilizes mixed integer nonlinear programming (MINLP) technique for minimizing the electricity cost and reducing the peak demand, by rescheduling the Loads, satisfying the industry constraints. The case study of a typical caustic-chlorine plant shows that a reduction of about 19% in the peak demand with a corresponding saving of about 3.9% in the electricity cost is possible with the optimal Load scheduling under TOU tariff.

  • Peak-Load Management in steel plants
    Applied Energy, 2006
    Co-Authors: S Ashok
    Abstract:

    Mini steel-plants in India, using electric-arc furnaces for steel manufacturing, are highly energy intensive. In the context of increasing electricity prices and the introduction of time varying electricity rates by utilities, mini steel-plants can reschedule their operations to reduce their electricity bills. This paper presents a Load model, which incorporates the characteristics of batch-type Loads common to any type of process industry. The model is coupled with an optimisation formulation utilising integer programming for minimising the total electricity-cost satisfying production, process flow and storage constraints for different tariff structures. The methodology proposed can be used for determining the optimal response for any industry under time varying tariffs. The case study of a steel plant shows that significant reductions in peak-period demand (about 50%) and electricity cost (about 5.7%) are possible with optimal-Load schedules. The utility can also get significant reduction in the peak coincident demand if large industries optimally reschedule their productions in response to time-of-use (TOU) tariff.

  • an optimization model for industrial Load Management
    IEEE Transactions on Power Systems, 2001
    Co-Authors: S Ashok, Rangan Banerjee
    Abstract:

    This paper presents a physically based model and formulation for industrial Load Management. The formulation utilizes an integer linear programming technique for minimizing the electricity costs by scheduling the Loads satisfying the process, storage and production constraints. The proposed strategy is evaluated by a case study for a typical flour mill with different Load Management options. The results show that significant reductions in peak electricity consumption are possible under time of use tariffs.

  • Load Management applications for the industrial sector
    Applied Energy, 2000
    Co-Authors: S Ashok, Rangan Banerjee
    Abstract:

    The goal of any Load-Management program is to maintain, as nearly as possible, a constant level of Load, thereby allowing the system Load factor to approach 100%. The important benefits of Load Management are reduction in maximum demand, reduction in power loss, better equipment utilisation and saving through reduced maximum demand charges. Load shifting, one of the simplest methods of Load Management, is to reduce customer demand during the peak period by shifting the use of appliances and equipment to partial peak and off-peak periods. Here no Loads are being switched off, but only shifted or rescheduled, and hence the total production is not affected. In this paper, a fully fledged program is developed for Load shifting and the same has been tried with the actual Load data collected from a typical fertiliser and chemical industry plant.

Rangan Banerjee - One of the best experts on this subject based on the ideXlab platform.

  • an optimization model for industrial Load Management
    IEEE Transactions on Power Systems, 2001
    Co-Authors: S Ashok, Rangan Banerjee
    Abstract:

    This paper presents a physically based model and formulation for industrial Load Management. The formulation utilizes an integer linear programming technique for minimizing the electricity costs by scheduling the Loads satisfying the process, storage and production constraints. The proposed strategy is evaluated by a case study for a typical flour mill with different Load Management options. The results show that significant reductions in peak electricity consumption are possible under time of use tariffs.

  • Load Management applications for the industrial sector
    Applied Energy, 2000
    Co-Authors: S Ashok, Rangan Banerjee
    Abstract:

    The goal of any Load-Management program is to maintain, as nearly as possible, a constant level of Load, thereby allowing the system Load factor to approach 100%. The important benefits of Load Management are reduction in maximum demand, reduction in power loss, better equipment utilisation and saving through reduced maximum demand charges. Load shifting, one of the simplest methods of Load Management, is to reduce customer demand during the peak period by shifting the use of appliances and equipment to partial peak and off-peak periods. Here no Loads are being switched off, but only shifted or rescheduled, and hence the total production is not affected. In this paper, a fully fledged program is developed for Load shifting and the same has been tried with the actual Load data collected from a typical fertiliser and chemical industry plant.

Bosong Li - One of the best experts on this subject based on the ideXlab platform.

  • Controllable Load Management approaches in smart grids
    Energies, 2015
    Co-Authors: Jingshuang Shen, Chuanwen Jiang, Bosong Li
    Abstract:

    With rapid smart grid technology development, the customer can actively participate in demand-side Management (DSM) with the mutual information communication between the distributor operation company and the smart devices in real-time. Controllable Load Management not only has the advantage of peak shaving, Load balance, frequency regulation, and voltage stability, but is also effective at providing fast balancing services to the renewable energy grid in the distributed power system. The Load Management faces an enormous challenge as the customer has a large number of both small residential Loads and dispersed renewable sources. In this paper, various controllable Load Management approaches are discussed. The traditional controllable Load approaches such as the end users’ controllable appliances, storage battery, Vehicle-to-Grid (V2G), and heat storage are reviewed. The “broad controllable Loads” Management, such as the microgrid, Virtual Power Plant (VPP), and the Load aggregator are also presented. Furthermore, the Load characteristics, control strategies, and control effectiveness are analyzed.

Yannchang Huang - One of the best experts on this subject based on the ideXlab platform.

  • integrating direct Load control with interruptible Load Management to provide instantaneous reserves for ancillary services
    IEEE Transactions on Power Systems, 2004
    Co-Authors: Kunyuan Huang, Yannchang Huang
    Abstract:

    This paper presents a novel adaptive control strategy for integrating direct Load control (DLC) with interruptible Load Management (ILM) to provide instantaneous reserves for ancillary services in deregulated power systems. Fuzzy dynamic programming is used to satisfy customers' requirements and yields a near optimal pre-scheduling of the DLC. Then, the energy payback associated with the DLC is further eliminated by the adaptive control strategy, which exploits interruptible Loads to modify the DLC schedule in real-time. Through the developed adaptive control strategy, the influences of the Load uncertainties and forecasting errors on the pre-scheduling of the DLC can also be excluded. The proposed algorithm was practically tested on the Taiwan power (Taipower) 38-unit system with 20 air-conditioner Loads and 15 interruptible Loads. The outcomes reveal that an exact amount of instantaneous reserves can be successfully acquired, and the results are robust against dynamic disturbances of the power system.

  • a model reference adaptive control strategy for interruptible Load Management
    IEEE Transactions on Power Systems, 2004
    Co-Authors: Kunyuan Huang, Hongchan Chin, Yannchang Huang
    Abstract:

    This paper presents a model reference adaptive control (MRAC) strategy for interruptible Load Management (ILM). The proposed MRAC strategy consists of fuzzy dynamic programming (FDP) and priority-based heuristics inference rule (PBHIR) to offer a flexible solution and a real-time adjusting scheme for the ILM problem. The customers' requirements can be dealt with by the fuzzy variables, and the optimal or near optimal schedule of interrupted Load can be acquired through DP search scheme. Additionally, a PBHIR is further employed as a regulator for the MRAC to modulate the schedule of the interrupted Load in real time. The influences of the Load variations and forecasting errors on the outputs of the FDP can be eliminated by the MRAC. The proposed MRAC strategy is tested practically on the Dar-Gun substation in Taiwan power (Taipower) system. The test results demonstrate the feasibility and effectiveness of applying the proposed method to the ILM problem.

Gilles Savard - One of the best experts on this subject based on the ideXlab platform.

  • A system architecture for autonomous demand side Load Management in smart buildings
    IEEE Transactions on Smart Grid, 2012
    Co-Authors: Giuseppe Tommaso Costanzo, Mariana Ferreira Dos Anjos, Guchuan Zhu, Gilles Savard
    Abstract:

    This paper presents a system architecture for Load Management in smart buildings which enables autonomous demand side Load Management in the smart grid. Being of a layered structure composed of three main modules for admission control, Load balancing, and demand response Management, this architecture can encapsulate the system functionality, assure the interoperability between various components, allow the integration of different energy sources, and ease maintenance and upgrading. Hence it is capable of handling autonomous energy consumption Management for systems with heterogeneous dynamics in multiple time-scales and allows seamless integration of diverse techniques for online operation control, optimal scheduling, and dynamic pricing. The design of a home energy manager based on this architecture is illustrated and the simulation results with Matlab/Simulink confirm the viability and efficiency of the proposed framework.