A.I. Cohen

Bio: A.I. Cohen is an academic researcher. The author has contributed to research in topics: Dynamic priority scheduling & Scheduling (computing). The author has an hindex of 1, co-authored 1 publications receiving 160 citations.

An optimization method for load management scheduling

Abstract: Many utilities have load management programs whereby they directly control residential appliances in their service area. An algorithm for scheduling the load control using dynamic programming is presented. This method is based on an analytic dynamic model of the load under control. The method can be used for different utility objectives, including minimizing production cost and minimizing peak load over a period of time. >

Demand Side Management in Smart Grid Using Heuristic Optimization

Abstract: Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

A Direct Load Control Model for Virtual Power Plant Management

Abstract: In the framework of liberalized electricity markets, distributed generation and controllable demand have the opportunity to participate in the real-time operation of transmission and distribution networks. This may be done by using the virtual power plant (VPP) concept, which consists of aggregating the capacity of many distributed energy resources (DER) in order to make them more accessible and manageable across energy markets. This paper provides an optimization algorithm to manage a VPP composed of a large number of customers with thermostatically controlled appliances. The algorithm, based on a direct load control (DLC), determines the optimal control schedules that an aggregator should apply to the controllable devices of the VPP in order to optimize load reduction over a specified control period. The results define the load reduction bid that the aggregator can present in the electricity market, thus helping to minimize network congestion and deviations between generation and demand. The proposed model, which is valid for both transmission and distribution networks, is tested on a real power system to demonstrate its applicability.

Microgrids - Large Scale Integration of Microgeneration to Low Voltage Grids

Abstract: Microgrids comprise Low Voltage distribution systems with distributed energy sources, such as micro-turbines, fuel cells, PVs, etc., together with storage devices, i.e. flywheels, energy capacitors and batteries, and controllable loads, offering considerable control capabilities over the network operation. These systems are interconnected to the Medium Voltage Distribution network, but they can be also operated isolated from the main grid, in case of faults in the upstream network. From the customer point of view, Microgrids provide both thermal and electricity needs, and in addition enhance local reliability, reduce emissions, improve power quality by supporting voltage and reducing voltage dips, and potentially lower costs of energy supply. This paper outlines selected research findings of the EU funded MICROGRIDS project (Contract ENK-CT-2002-00610). These include: • Development and enhancement of Microsource controllers to support frequency and voltage based on droops. Application of software agents for secondary control. • Development of the Microgrid Central Controller (MGCC). Economic Scheduling functions have been developed and integrated in a software package able to simulate the capabilities of the MGCC to place bids to the market operator under various policies and to evaluate the resulting environmental benefits. • Analysis of the communication requirements of the Microgrids control architecture • Investigation of alternative market designs for trading energy and ancillary services within a Microgrid. Development of methods for the quantification of reliability and loss reduction. • Initial measurements from an actual LV installation.

A Framework for Evaluation of Advanced Direct Load Control With Minimum Disruption

Abstract: The advent of advanced sensor technology and the breakthroughs in telecommunication open up several new possibilities for demand-side management. More recently, there has been greater interest from utilities as well as system operators in utilizing load as a system resource through the application of new technologies. With the wider application of demand-side management, there is an increasing emphasis on control of loads with minimum disruption. This paper develops a new framework for designing as well as assessing such an advanced direct load control program with the objective of minimizing end-user discomfort and is formulated as an optimization problem. With a fairly general setup for demand-side management, a simulation-based framework is developed for the stochastic optimization problem. Next, using this framework, insights into the effect of different parameters and constraints in the model on load control are developed.

Direct load control-A profit-based load management using linear programming

Abstract: Conventional cost-based load management ignores the rate structure offered to customers. The resulting cost savings may cause revenue loss. In a deregulated power industry where utilities absorb the ultimate consequence of their decision making, reexamination of load management must be conducted. In this paper, profit-based load management is introduced to examine generic direct load control scheduling. Based upon the cost/market price function, the approach aims to increase the profit of utilities. Instead of determining the amount of energy to be deferred or to be paid back, the algorithm controls the number of groups power customer/load type to maximize the profit. In addition to the advantage of better physical feel on how the control devices should operate, the linear programming algorithm provides a relatively inexpensive and powerful approach to the scheduling problem.