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Integration and Management Technique of Renewable Energy Resources in Microgrid

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Book cover Energy Harvesting and Energy Efficiency

Part of the book series: Lecture Notes in Energy ((LNEN,volume 37))

Abstract

Ever increasing demand for electricity supply along with higher power quality and reliability, available fossil fuels restrictions and environmental pollutions led to aggregation of clean energy resources (distributed generations) and developing microgrids. Integration of distributed generations such as wind power and solar energy are challenging with various problems such as non-deterministic nature of available wind power and solar energy. On the other hand, power systems are subject to other uncertainties such as load and energy prices in day-ahead (DA) and balancing markets. Hence, intermittence could be highlighted as the main obstacle of distributed generations’ aggregation which cause to imbalance charges set by uncertain market prices and accordingly economic losses. To this end, a comprehensive study should be performed to elaborate aforementioned issues. In this chapter, a stochastic model with the goal of profit maximization and imbalance cost minimization is presented. Unlike previous works, in the proposed model all existent uncertainties related to wind power, solar energy, load, day ahead and imbalance market prices altogether are considered by the means of scenario based investigations. In order to generate probable scenarios, uncertain parameters should be predicted. In this framework, a new method based on neural network theory is proposed for predicting wind speed and solar radiation. Afterwards, pumped-storage plant and demand response program are utilized as two complementary resources to compensate power imbalances. Storage devices are used as flexible resources to exchange power between low consumption—cheap hours and peak hours. Finally, to investigate efficiency of the proposed method two operating modes, namely coordinated and uncoordinated operation of clean energy resources , are assumed and testified on a test microgrid.

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Abbreviations

S:

Set of scenarios

T:

Settlement time period

\(\rho_{s}\) :

Probability of scenario s

\(P^{W\,max}\) :

Maximum energy of wind turbine, kW

\(P^{PV\,max}\) :

Maximum energy of PV system, kW

\(V^{UU}\) :

Maximum charging capacity of storage, kW

\(V^{DU}\) :

Minimum charging capacity of storage, kW

\(V^{UL}\) :

Maximum discharging capacity of storage, kW

\(V^{DL}\) :

Minimum discharging capacity of storage, kW

\(\eta_{1}\) :

Percentage of demanded load reduction

\(\eta_{2}\) :

Percentage of demanded load increase

\(Vf^{U}\) :

Primary and ultimate energy value of upper reservoir, kW

\(Vf^{L}\) :

Primary and ultimate energy value of bottom reservoir, kW

\(\eta\) :

Operation efficiency of pumped-storage unit in pumping and generating mode

N:

Number of the pumped-storage units

α:

Factor of load recovery

β:

Elasticity factor

\(\pi_{DA,t,s}\) :

Day-ahead electricity price at time period t and output scenario s, $/MWh

\(\alpha_{t,s}^{ + }\) :

Overproduction imbalance price ratio at time period t and output scenario s

\(\alpha_{t,s}^{ - }\) :

Underproduction imbalance price ratio at time period t and output scenario s

\(P_{t}^{WPP}\) :

Offered power by wind power producer in tth hour, kW

\(P_{t}^{PV}\) :

Offered power by PV system in tth hour, kW

\(P_{t}^{PUMP}\) :

Offered power by storage in tth hour, kW

\(D_{t}\) :

Offered power by demand response in tth hour, kW

\(P_{t,s}^{WPP}\) :

Real scheduled power of wind power in Sth scenario and tth hour, kW

\(P_{t,s}^{PV}\) :

Real scheduled power of PV system in Sth scenario and tth hour, kW

\(D_{t,s}\) :

Real scheduled demanded load in Sth scenario and tth hour, kW

\(g_{t,S}^{Pump}\) :

Discharge power output of storage in Sth scenario and tth hour s, kW

\(d_{t,S}^{Pump}\) :

Pumping power of storage in Sth scenario and tth hour, kW

\(t_{t,S}\) :

Binary variable to specify if the storage unit is capable to operate as a turbine or not in Sth scenario and tth hour

\(u_{t,S}\) :

Number of units running in pumping mode in Sth scenario and tth hour

\(y_{t,S} ,z_{t,s}\) :

Number of start-ups and shut-downs in Sth scenario and tth hour

DA:

Day-ahead

DG:

Distributed generation

DO:

Disjoint operation

MCP:

Marginal clearing price

MG:

Microgrid

MLP:

Multi-layer perceptron

MSE:

Mean square error

MT:

Micro turbine

NN:

Neural network

PV:

Photovoltaic

WT:

Wind turbine

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Authors and Affiliations

  1. Department of Technical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Hossein Shayeghi & Elnaz Shahryari

Authors
  1. Hossein Shayeghi
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  2. Elnaz Shahryari
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Corresponding author

Correspondence to Hossein Shayeghi .

Editor information

Editors and Affiliations

  1. Faculty of Electronics, Communication, and Computers, University of Piteşti, Piteşti, Romania

    Nicu Bizon

  2. Electrical Engineering Department, Faculty of Engineering, Seraj Higher Education Institute, Tabriz, Iran

    Naser Mahdavi Tabatabaei

  3. Department of Energy Technology, Aalborg University, Aalborg East, Denmark

    Frede Blaabjerg

  4. Department of Electrical and Electronics Engineering, Faculty of Technology, Gazi University, Ankara, Turkey

    Erol Kurt

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Shayeghi, H., Shahryari, E. (2017). Integration and Management Technique of Renewable Energy Resources in Microgrid. In: Bizon, N., Mahdavi Tabatabaei, N., Blaabjerg, F., Kurt, E. (eds) Energy Harvesting and Energy Efficiency. Lecture Notes in Energy, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-49875-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-49875-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49874-4

  • Online ISBN: 978-3-319-49875-1

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