Abstract
Further improvements in demand response programs implementation are needed in order to take full advantage of this resource, namely for the participation in energy and reserve market products, requiring adequate aggregation and remuneration of small size resources. The present paper focuses on SPIDER, a demand response simulation that has been improved in order to simulate demand response, including realistic power system simulation. For illustration of the simulator’s capabilities, the present paper is proposes a methodology focusing on the aggregation of consumers and generators, providing adequate tolls for the demand response program’s adoption by evolved players. The methodology proposed in the present paper focuses on a Virtual Power Player that manages and aggregates the available demand response and distributed generation resources in order to satisfy the required electrical energy demand and reserve. The aggregation of resources is addressed by the use of clustering algorithms, and operation costs for the VPP are minimized. The presented case study is based on a set of 32 consumers and 66 distributed generation units, running on 180 distinct operation scenarios.
Highlights
Demand Response (DR) is related to the modification of the electricity consumption pattern by end-use customers, in response to incentives or price signals, for economic or technical reasons when scheduled or called by the network or market operator
The present paper focuses on the simulation of DR programs and events
The presented work addressed the use of Distributed Generation (DG) and DR by a Virtual Power Players (VPP) in order to fulfil the energy and the reserve needs in the operation of a distribution network, minimizing the operation costs
Summary
Demand Response (DR) is related to the modification of the electricity consumption pattern by end-use customers, in response to incentives or price signals, for economic or technical reasons when scheduled or called by the network or market operator. The large integration of DG and DR resources brings several challenges related to the intermittence and unpredictability of these resources’ availability In this context, adequate attention is needed for the provision of reserves in the operation of power systems, in order to maintain increased levels of operation security [8]. A set of defined operation scenarios addressing the uncertainty on the main variables of the referred optimization problem clustering tools are applied in order define resource groups adequate for the operation of the network. This aggregation is done separately for DG and for DR resources, addressing the scheduled amounts of power for energy and for reserve.
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