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Sizing of microgrid components

Sizing of microgrid components

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A microgrid (MG) is a distinct energy system consisting of distributed energy resources (DERs) and loads having the ability to operate in parallel with, or independently from, the main power grid. MGs, which were initially introduced to ensure smooth operation and control of DERs in distribution networks, offer unprecedented economic and reliability benefits to electricity consumers with minimal carbon emission. These benefits, however, must be analysed and compared with the capital investment cost of the MG to ensure a complete return on investment and to justify the MG deployment. The biggest obstacle for the widespread and rapid deployment of MGs is the high capital investment cost of MGs. A true assessment of MGs economic benefits is a challenging task due to the significant uncertainties involved in the assessment. These uncertainties may include the intermittency of the renewable generation, the varying states of charge (SoC) of battery energy storage system (BESS), the uncertain demands, the varying market price, the probability of the MG islanding, the level of developer's risk-aversion and the unpredictably of the user preferences in the smart load management system. Moreover, some of the assessment metrics, such as the measure of reliability improvements are difficult to comprehend for consumers when represented in terms of the supply availability. Thus, efficient and optimum planning models are required to ensure the economic feasibility of MG deployments and to justify the investments based on cost-to-profit analysis under uncertain conditions. This chapter demonstrates a detailed model for the optimum sizing of MG components under the uncertainties involved in the system. The proposed model is validated with the simulation of several case studies conducted on a system depicting a similar MG in a medium-voltage (MV)-distribution system derived from electricity network of a power utility in New South Wales, Australia. The results from the case studies demonstrate the efficacy of the proposed model for the optimum sizing of the MG components to justify the MG deployment.

Chapter Contents:

  • 7.1 Microgrid components
  • 7.2 Microgrid sizing and profit maximization
  • 7.3 Models of distributed energy resources
  • 7.3.1 Probabilistic wind power output model
  • 7.3.2 Probabilistic photovoltaic power output model
  • 7.3.3 Dynamic battery energy storage power output model
  • 7.3.4 Micro-turbine power output model
  • 7.4 Optimal sizing of microgrid components
  • 7.4.1 Mathematical formulation
  • 7.4.2 Backtracking search optimization (BSO) algorithm
  • 7.4.3 Solution approach
  • 7.5 Case studies
  • 7.5.1 Case study 1
  • 7.5.2 Case study 2
  • 7.5.2.1 Scenario 1
  • 7.5.2.2 Scenario 2
  • 7.6 Summary
  • References

Inspec keywords: uncertain systems; profitability; power generation reliability; power grids; power generation planning; distributed power generation; power distribution economics; renewable energy sources; load management; power generation economics; pricing; battery management systems; investment

Other keywords: microgrid components; varying market price; optimum sizing; return on investment; MG economic benefits; battery state of charge; renewable generation; smart load management system; widespread deployment; distribution networks; minimal carbon emission; capital investment cost; reliability improvements; reliability benefits; distinct energy system; efficient planning models; uncertain demands; MG deployment; battery SoC; MG islanding probability; assessment metrics; battery energy storage system; electricity consumers; cost-to-profit analysis; optimum planning models; DER control; medium-voltage-distribution system; economic feasibility; high capital investment cost; distributed energy resources; smooth operation; MG components; rapid deployment; electricity network; main power grid

Subjects: Distribution networks; Power system management, operation and economics; Power system planning and layout; Optimisation techniques; Energy resources; Reliability; Distributed power generation

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