General Microgrid Energy Management Optimization Problem

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Grid-connected Microgrid

In a grid-connected MG, an MG is connected to a main grid or a network of multi-MGs. In such type, MG trades energy with main grid or other MGs to maximize its energy trading profit [62]. It buys energy when local generation is insufficient to meet load demand and injects excess power to main grid in case of local generation more than load demand and ESSs fully charged. As MG is connected to main grid, frequency at PCC voltage is set by main grid and MG cannot change it [63]. Moreover, it can act as a grid-supporting unit by adapting its power to provide ancillary services to the main grid network, depending on its state, for avoiding unstable.

Islanded Microgrid

Islanded MG, as the name indicates, is not connected to a main grid. This mode of operation can be temporary, when the MG is voluntarily disconnected from the main grid, or permanent. In such MGs, DERs ensure system voltage and frequency stability as well as supply demand balance. DERs are divided into categories of grid-following and grid-forming energy sources, where the latter are responsible for system voltage and frequency stability [64,65]. In islanded MG, at least one DER must be grid-forming for system control and stability. Hence, islanded MG must be properly designed and sizing should be done such that loss of load probability or load shedding can be reduced [66].
In remote areas without electricity, islanded MGs are considered as a best option to provide electricity [67–69]. For example, in [69], authors have studied seven cases in different parts of the world with different requirements to  show that utilizing the islanded MG approach immensely benefits the community.
Therefore, MGs are a promising solution for remote areas. Hybrid MGs incorporate RESs and conventional generation units, mostly diesel generators, in islanded communities, but fuel costs for such conventional units are high [69, 70].

Incentive-based DR

In incentive-based DR, consumers are given offers of payment to reduce their consumption when required by MG system. In this type of DR, consumers receive incentives to reduce their demand for ensuring system stability and reliability. Consumers can volunteer or be mandated to participate in such programmes. Once they have committed to the DR programme, they could be subject to penalties if they do not follow their contract [98]. Various approaches have been introduced in this category. In direct load control approach, the utility company or MG operator has remote access to some of the appliances and loads of the consumer to control them when required for system balancing [62, 99]. In another approach called interruptible/ curtailable load approach, consumers agree to reduce their load by a certain amount through decreasing or shutting down their interruptible/ curtailable loads. Consumer, committed in this approach, receives a discount or bill credit in return [100]. Demand biding, also called buyback, is another approach for incentive-based DR programmes, where large consumers usually participate and bid on their load reduction in the electricity market during peak hours when the price is at its peak [101]. In another approach, called Emergency Demand Reduction, consumers commit to reduce/shift their load in reaction to emergency instances in real-time. Such approach is mostly used to overcome the overloading of transmission and distribution network and can be considered as an ancillary service to the grid [102].

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Table of contents :

List of Publications
I Context
1 Background
1.1 Microgrid
1.2 Grid-connected Microgrid
1.3 Islanded Microgrid
2 Microgrid Components
2.1 PV System
2.2 Wind Turbine
2.3 Tidal Turbine
2.4 Energy Storage System
2.5 Loads
3 Demand Response
3.1 Incentive-based DR
3.2 Price-based DR
4 Microgrid Energy Management
4.1 Hierarchical Framework for Microgrid Operation
4.2 Microgrid Energy Management System
5 Optimization Problem
5.1 Linear Programming
5.2 Mixed-Integer Linear Programming
5.3 Non-Linear Programming
5.4 Mixed-Integer Non-Linear Programming
5.5 General Microgrid Energy Management Optimization Problem
6 Transactive Energy
7 Thesis Contributions
8 Thesis Outline
II Paper I
III Paper II
IV Paper III
V Paper IV
VI Paper V
VII Conclusion and Perspectives
1 Conclusions
2 Future Work


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