Optimal placement of battery swap stations in microgrids with micro pumped hydro storage systems, photovoltaic, wind and geothermal distributed generators

doi:10.1016/j.ijepes.2020.106483

International Journal of Electrical Power & Energy Systems

Volume 125, February 2021, 106483

https://doi.org/10.1016/j.ijepes.2020.106483 Get rights and content

Highlights

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Optimal placement of BSSs in microgrids is done.
•
Pumped hydro storage, PV, wind and geothermal power units are used in microgrid.
•
AC optimal power flow has been done and network constraints are satisfied.
•
The effects of different input parameters on microgrid operation are investigated.

Abstract

The penetration of electric vehicles (EVs) in vehicle markets is increasing; however long charging time in battery charging stations is an obstacle for larger adoption of EVs. In order to address this problem, battery swap stations (BSSs) have been introduced to exchange near-empty EV batteries with fully charged batteries. Refilling an EV in BSS takes only a few minutes. With decentralization of power systems, BSSs are typically connected to the microgrid (MG) in their neighborhood. Although the location of BSS in MG affects MG operation cost, to the best knowledge of the author, optimal placement of BSS has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro pumped hydro storage (PHS), photovoltaic, wind and geothermal units, while reactive power dispatch and all network constraints are considered by AC optimal power flow. The effect of BSS capacity and maximum charging/discharging power, BSS to MG link capacity, PHS capacity and maximum power of PHS unit on MG operation and optimal BSS location are investigated. DICOPT solver in general algebraic mathematical system (GAMS) is used to solve the formulated mixed-integer nonlinear optimisation problem.

Introduction

Microgrids (MGs) have been developed to enable optimal utilisation of distributed energy resources (DERs). MG is a cluster of distributed generation (DG) units, energy storage systems and loads that as a single controllable entity can operate either autonomously or connected to an upstream grid [1]. In remote areas where power grids are not accessible, isolated MGs may be set up to feed local demand and in areas that power grid is accessible, MGs are connected to the grid and utilise power exchange capability with grid to minimise their operation cost [2], [3], [4]. Thanks to high penetration of renewable energy resources, MGs are more environmental friendly than conventional grids and typically offer higher reliability than conventional grids [5], [6].

Transportation is a sector that emits a large portion of greenhouse gases and uses a lot of fossil fuels [7], [8], [9]. Transportation electrification was a revolution aimed to decrease the emission of greenhouse gases and also decrease the reliance on fossil fuels. The penetration of electric vehicles (EVs) is increasing; however the long charging time of EVs in battery charging stations (BCSs) is a barrier for their larger adoption [10]. In order to address this problem, battery swap stations (BSSs) were introduced to exchange near-empty EV batteries with fully charged batteries [10]. Refilling an EV in BSS takes only a few minutes and is not longer than refilling conventional vehicles in fuel stations. EVs that use BSSs do not purchase batteries, but batteries are leased to them, so the EV sticker price is decreased [11]. Lithium Ion is the most commonly used battery in EVs [12].

BSSs charge their batteries either with their own renewable power resources such as PV and wind resources or through a power grid/MG. In the latter case, they have the capability of power exchange with grid/MG. BSSs are able to charge their stocked batteries at low-price times. They offer advantages to power grids/MGs; through their battery to grid (B2G) capability, BSSs decrease operation cost of grid/MG, moreover, they can provide spinning reserve for grids/MGs [10]. An important point is that the location of BSS in a power system may affect power system operation cost and finding its optimal location is crucial, however this issue has been rarely addressed in the literature.

In [12], differential evolution (DE) with fitness sharing strategy has been used for optimal placement and sizing of BSSs from perspective of BSS owner, while the security constraints of power system are also considered. BSS planning problem has been formulated as a non-convex mixed-integer nonlinear programming (MINLP) and net present value (NPV) of the BSS project is minimised considering life cycle cost criterion. The penetration rate of EVs over time has been modeled as a geometric Brownian motion function and Monte Carlo Simulation (MCS) has been used for dealing with uncertainties. The results show that BSS causes a flatter demand profile for power system, while battery charging stations (BCSs) develop new peaks in demand profile.

In [13], grasshopper optimisation algorithm has been used to find optimal location and size of BSSs in power systems. Planning problem has been formulated as a multi-objective optimisation problem, while the objectives are energy loss and voltage stability, however the changes of electricity price over time has not been considered. Linear weighted sum (LWS) has been used for transforming the multi-objective optimisation problem into a single-objective optimisation problem. In order to facilitate charging of EVs in BSSs, power system has been sectionalized into multiple zones. The results show that grasshopper optimisation algorithm performs better than particle swarm optimisation (PSO), gravitational search algorithm and artificial bee colony. In [14], the route and BSS location are determined for a fleet of EVs so that investment cost of BSSs and shipment cost of EVs are minimised. The emission of EVs is to deliver goods to customers at different nodes. Each customer must be visited by one and only one EV. CPLEX as a mixed-integer programming (MIP) solver and Tabu search are used for solving the formulated optimisation problem.

Nowadays, with decentralization of power systems and increase in the number of MGs, BSSs are commonly connected to the MG in their neighborhood. Considering the reviewed literature, to the best knowledge of the author, optimal placement of BSS in MGs has not been done from the perspective of MG. Therefore, in this paper, the objective is to find optimal location of BSSs in a MG with micro PHS unit, photovoltaic (PV), wind and geothermal DG units, while all network constraints and reactive power dispatch are considered. It is assumed that all facilities except for thermal DGs are owned by MG owner and the optimisation is done from MG perspective. DICOPT solver in general algebraic mathematical system (GAMS) has been used for solving the formulated mixed-integer nonlinear optimisation problem [15], [16]. The contributions of the paper are listed out as below.

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Optimal placement of BSSs in MGs is done from perspective of MG.
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Micro PHS unit as an energy storage system (ESS) and PV, wind and geothermal power units are used in the MG.
•
AC optimal power flow is done and all network constraints are satisfied.
•
The effect of BSS capacity, BSS to MG link capacity, micro PHS unit capacity and maximum power of micro PHS unit on MG operation are investigated.

The case study is a MG with two PV units, two wind units, two geothermal units, one micro PHS unit and one BSS. The rest of the paper is organised as follows; in section 2, the performance of renewable components of the MG and PHS units are explained. In section 3, optimal placement of BSS in MGs with PHS, PV, wind and geothermal units is formulated as a MINLP. The results and analysis of results can be found in section 4. Finally, section 5 contains the conclusions of the paper.

Section snippets

MG components

In this section, the components of the MG studied in this paper are introduced. Small-scale wind and PV units are renewable and sustainable energy resources that are commonly used in MGs. In wind generation units, wind energy is used to turn turbines of distributed generators and in PV units, PV cells directly convert sunlight into electric power. Both wind and PV units represent volatile and intermittent sources of power and thanks to technological advances, have reached grid parity in very

Problem formulation

The model for planning BSSs in a grid-connected MG with PHS, PV, wind, geothermal and thermal DGs is characterised by (1)-(28). This is a MINLP model, wherein the planning cost as the sum of BSS investment cost and MG operation cost is minimised. MG operation cost consists of the cost of power purchased from thermal DGs, cost of power imported from upstream grid and cost of load shedding is minimised. Operation cost of PV, wind and geothermal units is assumed zero. $OF = I N V + O P$ $O P = \sum_{t} \sum_{j} {b i d}_{j t} P_{j t} Δ t + \sum_{t} P_{g}$

Results and analysis

A MG with 33 buses, 32 branches, 4 thermal DGs, 2 PV units, 2 wind units, 2 geothermal power units and one PHS unit has been used as the case study. Since the objective is to find optimal location of BSSs with known size and investment cost is constant, the best plan would be the one minimising daily operation cost of the MG. Bus and branch data of the MG is available in [30] and its single line diagram can be seen as Fig. 1. Data of thermal DGs can be found in Table 1. Table 2 contains

Conclusions

In this paper, optimal location of BSSs has been determined in MGs with PHS, photovoltaic, wind and geothermal units, while reactive power dispatch, DGs constraints and all network constraints are considered in AC optimal power flow. AC optimal power flow has been formulated as a mixed-integer nonlinear optimisation problem that the voltages of buses at different times, schedule of DGs, BSS and PHS unit, schedule of power exchange with grid, load shedding schedule and BSS location form decision

CRediT authorship contribution statement

A. Rezaee Jordehi: Conceptualization, Methodology, Software, Validation, Writing - review & editing, Data curation, Writing - original draft. Mohammad Sadegh Javadi: Software, Visualization. João P. S. Catalão: Project administration, Investigation, Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

There is no conflict of interest for this paper. J.P.S. Catalão acknowledges the support by FEDER funds through COMPETE 2020 and by Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 (02/SAICT/2017).

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Optimal placement of battery swap stations in microgrids with micro pumped hydro storage systems, photovoltaic, wind and geothermal distributed generators

Highlights

Abstract

Introduction

Section snippets

MG components

Problem formulation

Results and analysis

Conclusions

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgements

The Electricity Journal

J Cleaner Prod

Energy

Energy

Comput Oper Res

Appl Energy

Energy

Renewable Energy

Sol Energy Mater Sol Cells

Renew Sustain Energy Rev

Energy Policy

Dynamic environmental-economic load dispatch in grid-connected microgrids with demand response programs considering the uncertainties of demand, renewable generation and market price

Int J Numer Model Electron Networks Devices Fields

Mixed binary-continuous particle swarm optimisation algorithm for unit commitment in microgrids considering uncertainties and emissions

International Transactions on Electrical Energy Systems

Co-optimized bidding strategy of an integrated wind-thermal-photovoltaic system in deregulated electricity market under uncertainties

J Cleaner Prod

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IET Renew Power Gener