A clusterized energy management with linearized losses in the presence of multiple types of distributed generation

Abstract

This paper presents an optimal management (OM) strategy for distributed generation (DG) planning whose objective is the CO2 reduction for the power generation on the Jurong Island in Singapore. Different DG resources are investigated with solar generation, energy storage, small gas turbine as well as controllable loads in addition to the centralized generation already in site. Each of those resource is modeled in an optimal scheduling method that allow to test a great number of different DG configurations (i.e. type/size/site). The paper mainly focuses on the OM implementation that would allow avoiding prohibitive computational times. At first it is achieved thanks to a linearization of the line losses and a modified DC power flow. When simulating the system over a representative day. Then a generic clustering method is developed as well as a sequential optimal management (S-OM) with the zonal and nodal models is implemented. Different validation tests are performed as well as sets of simulation with different DG configurations. The optimal DG planning procedure will be part of further developments.

Embedding Energy Storage Systems into a Dynamic Knowledge Graph

“…In this use case, we have chosen the photovoltaic generator located in Singapore Semakau Island and the electrical network in Singapore Jurong Island. 38 The coordination agent then calls the energy storage selection agent. The energy storage selection agent is created by packaging the ESS selection framework proposed by Li et al 22 (described in Section 2.5) as an agent that can apply the Semantic Web stack to interact with the knowledge graph.…”

“…The agent does not return any output and terminates upon modifying the knowledge graph. In this use case, we have chosen the photovoltaic generator located in Singapore Semakau Island and the electrical network in Singapore Jurong Island …”

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

“…Physical system DG and microgrids [4,5,8,38,45,58,82,115,[170][171][172][173][174][175] Physical upgrade and revitalisation [7,64,90,115,118,123,124,176] Planning of switch installation [138,177] Energy interconnection and multi energy cooperation [15,16,54,[140][141][142][143][144] Vegetation management [178,179] Selective undergrounding [180][181][182] Emergency resources and repair station planning [46,67,183] Substation elevated and relocation [184] Cyber system Intelligent device layout, situation awareness and status analysis [4,22,[185][186][187] Optimise the configuration of PMU and communication facilities [188,...…”

An iterative linear DistFLow for dynamic optimization in distributed generation planning studies

“…DG planning then refers to the optimal allocation of the resources in terms of type, size and site [2], which have been extensively addressed in the literature for the past two decades. One main technical challenge is that the considered systems have to be simulated a great number of times, and with different DG configurations before finding the best solution [3], which may lead to prohibitive computational times. Especially, solving the nonlinear load flow equations over long time horizons may result in non-tractable problems as pointed out in [4] and [5].…”