Research Article: Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation

Date Published: February 14, 2019

Publisher: Public Library of Science

Author(s): Muhammad Sufyan, Nasrudin Abd Rahim, ChiaKwang Tan, Munir Azam Muhammad, Siti Rohani Sheikh Raihan, Yang Li.


The incessantly growing demand for electricity in today’s world claims an efficient and reliable system of energy supply. Distributed energy resources such as diesel generators, wind energy and solar energy can be combined within a microgrid to provide energy to the consumers in a sustainable manner. In order to ensure more reliable and economical energy supply, battery storage system is integrated within the microgrid. In this article, operating cost of isolated microgrid is reduced by economic scheduling considering the optimal size of the battery. However, deep discharge shortens the lifetime of battery operation. Therefore, the real time battery operation cost is modeled considering the depth of discharge at each time interval. Moreover, the proposed economic scheduling with battery sizing is optimized using firefly algorithm (FA). The efficacy of FA is compared with other metaheuristic techniques in terms of performance measurement indices, which are cost of electricity and loss of power supply probability. The results show that the proposed technique reduces the cost of microgrid and attain optimal size of the battery.

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In the last few decades, the world is seeing an unprecedented rise in its population with the resultant subsequent excessive power demand, both of which are the main operative factors behind global warming and carbon emissions. Unfortunately, we are still adamantly depending on the usage of fossil fuels which incidentally are still playing the major role in supplying energy for the power generation and transportation system. However, continual and inevitable depletion of fossil fuel resources in the recent years has put a serious pressure to bear on the governments and energy entrepreneurs to be responsible enough to move towards replenishment of energy through RES [1]. Greenhouse gas emission is reduced by replacing fossil fuels with renewable energy and leads to a growth in the industrial sector. However, the intermittent nature of RES is thwarting the stability of the power system in the economic sense of the word, hence, efficiently controlled methods have become the order of the day to overcome the issues of voltage disturbances, frequency regulations and network security during the high penetration of the RES to meet the growing demand of the population at large [2]. Table 1 shows all the nomenclature that would be used in this paper.

A hybrid isolated microgrid system contains three subsystems: the power demand, the power generation, and the power distribution subsystem. These subsystems have major impact on the cost of the microgrid system. They are dependent on the climatic conditions and the consumer services. This section presents the power and cost models for the wind, solar, diesel generator and energy storage as the DERs of the power generation subsystem, load profile of the residential area as the demand subsystem and the microgrid itself is configured as the power distribution subsystem. The combination of different RESs improves the system efficiency and reduces the requirements of energy storage as compared to a single RES. The general schematic of the microgrid system containing the three systems is as shown in Fig 1.

The power management strategy of the microgrid has a direct impact on the operational behavior of the system regardless of grid-connected or isolated mode of operation. However, in the isolated mode the power generated from the distributed resources must satisfy the load demand for a secure and reliable operation; otherwise, the system will face load shedding which will increase the cost in term of power losses. The unavailability of DERs at certain times of the day will force the diesel generator and battery storage to operate and dispatch optimal power. Moreover, an excess power generation by renewable resources necessitates the charging of the battery. The extra energy after charging is dissipated into dump load to avoid overcharging of batteries. Thus, an efficient power management strategy is required to dispatch the power at the lowest cost to reliably serve the load considering the technical constraints of the microgrid. The power strategy for economic scheduling in this paper has been summarized into the following scenarios:

The above-mentioned power management strategy is implemented to obtain an optimal battery size and daily economic scheduling of microgrid. The capital cost of battery constitutes a major factor in calculating the battery size. The optimal BESS sizing is obtained by minimizing the daily scheduling cost of the microgrid and BESS total cost per day. Hence, the objective function of the microgrid is the total operating cost given by the expression
The scheduling cost for the day is the sum of the cost of three diesel generators dispatching power to fulfil the load demand and the cost of charging/discharging the battery storage. In this study, N is taken as three while the time period T is formulated as 24 hours. The TCPD of battery storage is the function of battery capital cost and yearly maintenance cost accounted for the lifetime of battery. The optimal battery size will minimize the total cost of microgrid.

The proposed method calculates the optimal battery size and performs economic scheduling of the distributed generators as per load demand at each hour. The economic scheduling is based on the power management strategies discussed above and is similar to the conventional method when there is an excess of energy through the renewable sources than that required by loads such as in scenarios 1 and 2 of the power management strategies. However, when the load power is greater than the renewable energy generation, the proposed algorithm will dispatch the power from diesel generators or the energy storage. The decision is based on the depth of discharge of the energy storage, which affects the operational cost of storage. The algorithm reads the DOD value at the start of the interval and then transforms the cost function given in Eq (7) from 3-dimensional to 2-dimensional plot.

The firefly algorithm (FA) analyzes the social behavior of flies and is similar to other meta-heuristic techniques. The algorithm was originally developed by Yang [32] based on three main ideas:

A typical low voltage microgrid with three diesel generators and a lithium-ion battery is analyzed in this study to illustrate the performance of the proposed energy management strategies. The microgrid consists of 68 kW photovoltaic and 37 kW wind turbine system. The data for the diesel generators are taken from [33] and is shown in Table 2. The battery capital cost, maintenance cost, interest rate and lifetime had been taken from [34]. The parameters for the wind turbine, solar photovoltaic, energy storage and optimization algorithm are represented in Tables 3–6 respectively. The proposed method is formulated in MATLAB (R2016b) and run on the personal computer 2.6 GHz core i5 processor with 6 GB RAM. The computational time involved in the simulation is about 3 min 15 sec.

As more energy supplies are predicted to utilize renewable sources, the economic and battery sizing aspects of the energy storage in the isolated microgrid has to be taken into consideration to ensure a reliable service. The present study has solved the economic scheduling problem between the diesel generators and the battery storage considering real time battery degradation cost. One of the strengths of the proposed method is to charge the battery when the DOD value is high so that the battery is not depleted during critical hours. Firefly optimization algorithm was implemented to solve the economic dispatch and the battery sizing problem. The simulation results reveal that the microgrid faces the load shedding without the battery storage resulting in high operating cost and instability. Furthermore, a large BESS size does not minimize the operating cost, but there exists an optimal point, which should be considered when designing a microgrid system. The lifespan of the battery is also extended when optimal size is selected for economic scheduling saving cost of replacing BESS. The proposed method has been compared with other existing methods and 50% reduction in operating cost has been recorded. Thus, the obtained results show that ignoring the depth of discharge and lifetime of the BESS in an economic scheduling will inflate the operating cost of the microgrid. The energy scheduling approach presented will help the independent power plant operators to perform the rural electrification efficiently and prolong the battery lifetime.




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