Demand response

Summary

Demand response is a change in the power consumption of an electric utility customer to better match the demand for power with the supply. Until the 21st century decrease in the cost of pumped storage and batteries electric energy could not be easily stored, so utilities have traditionally matched demand and supply by throttling the production rate of their power plants, taking generating units on or off line, or importing power from other utilities. There are limits to what can be achieved on the supply side, because some generating units can take a long time to come up to full power, some units may be very expensive to operate, and demand can at times be greater than the capacity of all the available power plants put together. Demand response, a type of energy demand management, seeks to adjust in real-time the demand for power instead of adjusting the supply. Utilities may signal demand requests to their customers in a variety of ways, including simple off-peak metering, in which

Official source

https://en.wikipedia.org/wiki/Demand_response

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As part of the transition to a future power grid, distribution systems are undergoing profound changes evolving into Active Distribution Networks (ADNs). The presence of dispersed generation, local storage systems and responsive loads in these systems incurs severe impacts on planning and operational procedures. This paper focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to voltage regulation and lines congestion management. We identify the main challenges and opportunities related to ADNs control and we discuss recent advances in this area. Finally, we describe a broadcast-based unified control algorithm designed to provide ancillary services to the grid by a seamless control of heterogeneous energy resources such as distributed storage systems and demand-responsive loads. (C) 2016 The Author. Published by Elsevier Ltd.

Elsevier Science Bv2016

Optimum dispatch of a multi-storage and multi-energy hub with demand response and restricted grid interactions

Timothée Chatelain, Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

Integrated energy systems such as multi energy hubs which combines different energy conversion technologies and storage is recently getting popular. These systems possess the potential to integrate distributed renewable energy sources with a minimum impact to the grid. Hence, a number of recent studies have focused on optimizing the operation of energy hubs with simple energy systems. This study focuses on optimizing the dispatch of a multi-energy hub model which includes solar PV (SPV) panels, wind turbines, boiler, Internal Combustion Generator (ICG), Cogeneration plant (CHP), energy storage (heat and electricity). The energy hub is considered to maintain limited interactions with the grid. Interactions with e-mobility is considered as a flexible demand. A detailed energy hub model is developed considering energy interactions among all the aforementioned components. Dispatch strategy is optimized using evolutionary algorithm. Results obtained from the evolutionary algorithm is compared with Particle Swarm Optimization (PSO) algorithm. A detailed analysis is conducted in order to assess the energy interactions within the system. Sensitivity of system components such as storage size, renewable energy capacity etc., grid interactions, and time horizon considered for optimization is subsequently assessed and reported concisely. Results obtained clearly shows choice of dispatch strategy is considerably volatile which makes the operation of the energy hub challenging. This can be mitigated up to a certain level by increasing the capacity of battery bank.

2017

Primary Voltage Control in Active Distribution Networks via Broadcast Signals: The Case of Distributed Storage

Maryam Bahramipanah, Konstantina Christakou, Jean-Yves Le Boudec, Mario Paolone, Dan-Cristian Tomozei

In this paper we consider an Active Distribution Network (ADN) that performs primary voltage control using realtime demand response via a broadcast low-rate communication signal. Additionally, the ADN owns distributed electrical energy storage. We show that it is possible to use the same broadcast signal deployed for controlling loads to manage the distributed storage. To this end, we propose an appropriate control law embedded into the distributed electrical storage controllers that reacts to the defined broadcast signal to control both active and reactive power injections. We analyze, in particular, the case where distributed electrical storage systems consist of supercapacitor banks and where the ADN uses the Grid Explicit Congestion Notification (GECN) for real-time demand response developed by the authors in a previous contribution. We estimate the energy reserve required for successfully performing voltage control depending on the characteristics of the network. The performance of the scheme is numerically evaluated on the IEEE 34-node test feeder. We further evaluate the effect of reactive versus active power control depending on the line characteristics. We find that without altering the demand-response signal, a suitably designed controller implemented in the storage devices allows them to successfully contribute to primary voltage control.

2013