Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House

Abstract

Demand response as a distributed resource has proved its significant potential for power systems. It is capable of providing flexibility that, in some cases, can be an advantage to suppress the unpredictability of distributed generation. The ability for participating in demand response programs for small or medium facilities has been limited; with the new policy regulations this limitation might be overstated. The prosumers are a new entity that is considered both as producers and consumers of electricity, which can provide excess production to the grid. Moreover, the decision-making in facilities with different generation resources, energy storage systems, and demand flexibility becomes more complex according to the number of considered variables. This paper proposes a demand response optimization methodology for application in a generic residential house. In this model, the users are able to perform actions of demand response in their facilities without any contracts with demand response service providers. The model considers the facilities that have the required devices to carry out the demand response actions. The photovoltaic generation, the available storage capacity, and the flexibility of the loads are used as the resources to find the optimal scheduling of minimal operating costs. The presented results are obtained using a particle swarm optimization and compared with a deterministic resolution in order to prove the performance of the model. The results show that the use of demand response can reduce the operational daily cost.

Reliability Evaluation of PV Systems with Integrated Battery Energy Storage Systems: DC-Coupled and AC-Coupled Configurations

“…Nonetheless, as stated in [5], higher efficiency of power electronic units nowadays has caused the difference in system efficiency to become smaller compared to the early stage of development of the PV-battery systems. In [6], the residential PV-battery systems were studied from a control point of view. A techno-economic analysis of a PV-battery system is investigated for the installation site in Greece [7] with DC-coupled configuration.…”

Distributed Energy Resources Management 2018

“…This paper provides a summary of the Special Issue of Energies covering the published articles [1][2][3][4][5][6][7][8][9][10][11][12][13], which address several topics related to distributed energy resources management. Table 1 identifies the most relevant topics in each publication of the 2018 Edition.…”

“…Finally, in [13], optimal energy resource management at the residential house level was obtained using particle swarm optimization, as proposed by Faia et al The energy storage unit was managed by considering the available photovoltaic generation, the energy cost, and the use of flexible loads.…”

“…The Special Issue "Distributed Energy Resources Management 2018" includes 13 papers [1][2][3][4][5][6][7][8][9][10][11][12][13], and is a continuation of the Special Issue "Distributed Energy Resources Management" published in https://www.mdpi.com/journal/energies/special_issues/distributed_energy_resources_ management2018. The success of the previous Special Issue, for which the summary is published in [14], shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level, and at the wide power system level.…”

Optimal management of demand response aggregators considering customers' preferences within distribution networks

“…In another work [17], the authors solved the distribution grid congestion by DR while considering customers’ preferences. DR is optimised for smart houses by particle swarm algorithm in [18]. Households in microgrids are also optimised DR with non‐linear auto‐regressive neural network in [19].…”