Optimization of the Operational Cost and Environmental Impact of a Multi-Microgrid System

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Energy Procedia 158 Energy Procedia 00(2019) (2017)3827–3832 000–000 www.elsevier.com/locate/procedia

10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, 10th International Conference on Applied Energy China(ICAE2018), 22-25 August 2018, Hong Kong, China

Optimization of the Operational Cost and Environmental Impact of Optimization Operational Costonand Environmental Impact of The of 15ththe International Symposium District Heating and Cooling a Multi-Microgrid System a Multi-Microgrid System Assessing the feasibility of using the heat demand-outdoor Tiaan Gildenhuys, Lijun Zhang*, Xianming Ye and Xiaohua Xia Tiaan Gildenhuys, Lijun Xianming Ye and Xiaohua Xia forecast temperature function for a Zhang*, long-term district heat demand Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield, Pretoria 0002, South Africa Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Hatfield, Pretoria 0002, South Africa

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

a

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France Abstract c Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract A multi-objective optimization model is developed for a multi-microgrid system, which not only minimizes its operational cost A optimization model is of developed for a model multi-microgrid system, which not minimizes its operational costa butmulti-objective also the emissions. The performance the proposed is evaluated by comparing itsonly results to the results provided by but also the emissions. The performance theminimizes proposed model is evaluated byBoth comparing itsmodels results are to the resultsonprovided by a single-objective optimization model that of only the operational cost. of these applied a case study Abstract single-objective optimization model thatfunctions only minimizes operational cost.algorithm, Both of these models applied onThe a case study and solved through the use of hybrid betweenthe Matlab’s genetic fmincon andare fgoalattain. proposed and solved through the usemanagement of hybrid functions between Matlab’s genetic fgoalattain. The proposed model identified an energy plan for the multi-microgrid system algorithm, in the case fmincon study thatand increases the operational cost District heating an networks are commonly addressed in the literaturesystem as oneinofthethe most effective solutions decreasing the model identified energythe management plan for the multi-microgrid case study that increases thefor operational cost by 19.4% but it decreases emissions by 73.9%. emissionsthefrom the building sector. These systems require high investments which are returned through the heat bygreenhouse 19.4% butgas it decreases emissions by 73.9%. sales. Due to the changed climate conditions and building renovation policies, heat demand in the future could decrease, Copyright © 2018 Elsevier Ltd. All rights reserved. © 2019 The Published by Elsevier Ltd. prolonging the investment return Copyright ©Authors. 2018 Elsevier Ltd. Allperiod. rights reserved. Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied This ismain an open access article under the CCthe BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) The scope of this paper is to assess feasibility using the heat demand – outdoor temperature Conference function for on heatApplied demand Selection and peer-review under responsibility of the of scientific committee of the 10th International Energy (ICAE2018). Peer-review under responsibility of the scientific committee of ICAE2018 The as 10th Conference forecast. The district of Alvalade, located in Lisbon (Portugal), was –used a International case study. The district on is Applied consistedEnergy. of 665 Energy (ICAE2018). buildingsMicrogrid; that varyMulti-microgrid in both construction period andmicrogrids; typology.Operational Three weather scenariosOptimization. (low, medium, high) and three district Keywords: system; Networked Cost; Emissions; renovation scenarios were developed (shallow, intermediate, deep). To Cost; estimate the error, obtained heat demand values were Keywords: Microgrid; Multi-microgrid system; Networked microgrids; Operational Emissions; Optimization. compared with results from a dynamic heat demand model, previously developed and validated by the authors. results showed that when only weather change is considered, the margin of error could be acceptable for some applications 1.The Introduction error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation 1.(the Introduction scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). A value microgrid (MG) refers to a collection of controllable distributed (dieselthat generators, microThe of slope coefficient increased on average within the range of 3.8%generating up to 8% units per decade, corresponds to the A microgrid (MG) refers to a collection of controllable distributed generating units (diesel generators, microturbines, fuel cells, etc.), uncontrollable distributed generating units (wind turbines, photo-voltaic systems, etc.), decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and turbines, cells,storage etc.), uncontrollable distributed generating units (wind turbines, systems, etc.), distributed energy systems (batteries, capacitors, flywheels, etc.) for and loads photo-voltaic that form a single renovationfuel scenarios considered). On the other hand, function intercept increased 7.8-12.7% per together decade (depending on the distributed energy storage systems (batteries, flywheels, and loadsforthat a power single controllable system The usedvalues to supply heat and to atolocal area Aetc.) grid-connected microgrid canform import coupled scenarios). suggested couldpower becapacitors, used modify the[1]. function parameters the together scenarios considered, and controllable system used to supply heat and power to a local area [1]. A grid-connected microgrid can import power from and the export power to the main estimations. grid; however, a microgrid can also operate in island mode which isolates it from improve accuracy of heat demand

from and export power to the main grid; however, a microgrid can also operate in island mode which isolates it from © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. * Corresponding author. Tel.:+27 012 420 2674; fax: +27 012 362 5000. * Corresponding Tel.:+27 012 420 2674; fax: +27 012 362 5000. E-mail address:author. [email protected] Keywords: Heat demand; Forecast; Climate change E-mail address: [email protected]

1876-6102 Copyright © 2018 Elsevier Ltd. All rights reserved. 1876-6102 Copyright © 2018 Elsevier Ltd. All of rights reserved. committee of the 10th International Conference on Applied Energy (ICAE2018). Selection and peer-review under responsibility the scientific Selection and peer-review under responsibility of the scientific committee of the 10th International Conference on Applied Energy (ICAE2018). 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. 1876-6102 © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of ICAE2018 – The 10th International Conference on Applied Energy. 10.1016/j.egypro.2019.01.865

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the main grid [1]. A microgrid can also be connected to another microgrid to form a multi-microgrid system that provides the microgrids with an opportunity to share power amongst one another [2]. The optimal energy management plan of a multi-microgrid system can be identified through the use of an optimization model. However, existing optimization models only minimize the overall operational cost of a multi-microgrid system and do not consider the emissions such as sulfur dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2), etc. These emissions should not be ignored as they have substantially contributed towards climate change, which will cause severe droughts, hurricanes, flooding, change in the rainfall pattern, increase in sea levels, etc. [3]. As a result, it is recommended that an optimization model should be developed to identify the optimal energy management plan of a multi-microgrid system, which not only minimizes the operational cost but also the emissions. Nomenclature Ci Ci,grid(t) Ci,j(t) Ei Ei,grid,s(t) Ei,x,s(t) FCi,x(t) Ii,grid(t) Ii,j(t) LDi(t) M OMi,x(t) OMi,y(t) OMi,k(t) Pi,xCon(t) Pi,xConMin Pi,xConMax Pi,k ESS(t) Pi,kESSMin Pi,kESSMax Pi,grid(t) Pi,gridMin Pi,gridMax Pi,jMG(t) Pi,jMGMin Pi,jMGMax Pi,yUncon(t) Rdown,x,i Rup,x,i SDi,x(t) SOCi,kESS(t) SOCi,kESSMin SOCi,kESSMax SUi,x(t) T Ui V t

number of controllable/dispatchable generating units within the i-th microgrid cost associated with the i-th microgrid for purchasing power from the main grid cost associated with the i-th microgrid for purchasing power from the j-th microgrid number of distributed energy storage systems within the i-th microgrid mass of the s-th emission emitted by the main grid’s coal power plants mass of the s-th emission emitted by the i-th microgrid’s x-th controllable generating unit fuel cost of the i-th microgrid’s x-th controllable generating unit during time instance t income received by the i-th microgrid for selling power to the main grid income received by the i-th microgrid for selling power to the j-th microgrid load demand associated with the i-th microgrid number of individual microgrids operational and maintenance cost of the i-th microgrid’s x-th controllable generating unit operational and maintenance cost of the i-th microgrid’s y-th uncontrollable generating unit operational and maintenance cost of the i-th microgrid’s k-th energy storage system output power of the i-th microgrid’s x-th controllable generating unit minimum output power of the i-th microgrid’s x-th controllable generating unit maximum output power of the i-th microgrid’s x-th controllable generating unit power flowing from or to the i-th microgrid’s k-th energy storage system minimum power flow from or to the i-th microgrid’s k-th energy storage system maximum power flow from or to the i-th microgrid’s k-th energy storage system power flowing between the main grid and i-th microgrid minimum power flow between the main grid and i-th microgrid maximum power flow between the main grid and i-th microgrid power flowing between the i-th microgrid and j-th microgrid minimum power flow between the i-th microgrid and j-th microgrid maximum power flow between the i-th microgrid and j-th microgrid output power of the i-th microgrid’s y-th uncontrollable generating unit lower ramp rate limit of the i-th microgrid’s x-th controllable generating unit upper ramp rate limit of the i-th microgrid’s x-th controllable generating unit shutdown cost of the i-th microgrid’s x-th controllable generating unit state of charge of the i-th microgrid’s k-th energy storage system minimum state of charge of the i-th microgrid’s k-th energy storage system maximum state of charge of the i-th microgrid’s k-th energy storage system start-up cost of the i-th microgrid’s x-th controllable generating unit number of time instances number of uncontrollable/nondispatchable generating units within the i-th microgrid number of different types of emissions time interval



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2. Proposed multi-objective optimization model 2.1. Objective functions The first objective function minimizes the operational cost of a multi-microgrid system and is given by Ci Ci Ci   Ci        FC t OM t SU t             i, x    i, x    i , x    SDi , x  t       x 1 x 1 x 1 x 1  Ei M  T  Ui M  minimize FTotal       OM i , y  t     OM i ,k  t     Ci , j  t   I i , j  t    .  i k 1 1  t 1   j 1  y1  j i      Ci , grid  t   I i , grid  t      

The second objective function minimizes the emissions and is given by

 minimize ETotal

 T  V  Ci      Ei , x ,s  t    Ei , grid , s  t    .  i 1   t 1  x 1     s 1 M

2.2. Decision variables    

The proposed optimization model includes several decision variables, namely. Output power of the i-th microgrid’s x-th controllable generating unit (Pi,xCon(t)). The amount power flowing from or to the i-th microgrid’s k-th energy storage system (Pi,kESS(t)). The amount of power flowing between the i-th microgrid and j-th microgrid (Pi,jMG(t)). The amount of power flowing between the main grid and i-th microgrid (Pi,grid(t)).

2.3. Constraints The constraint that ensures there is a balance between the load demand and supply is given by Ci

Ui

Ei

M

  Uncon  t     Pi ,ESS   MG  LDi  t  .   Pi,Con x  t      Pi , y k  t    Pi , grid  t     Pi , j  t   

 x 1 y 1  k 1

j 1

The lower and upper boundary constraints of the decision variables are given by ConMax Pi ,ConMin  Pi ,Con , x x  t   Pi , x Max Pi ,Min grid  Pi , grid  t   Pi , grid ,

MGMax Pi ,MGMin  Pi ,MG , j j  t   Pi , j ESSMax Pi ,ESSMin  Pi ,ESS . k k  t   Pi ,k

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The ramp rate constraints of the i-th microgrid’s x-th controllable generating unit are given by Con Pi ,Con x  t   Pi , x  t  1  Rup ,i , x t ,

Con Pi ,Con x  t  1  Pi , x  t   Rdown ,i , x t.

The constraint that ensures the state of charge of an energy storage system does not exceed its minimum and maximum limits is given by ESSMax SOCiESSMin  SOCiESS . ,k , k  t   SOCi , k

A constraint is required to ensure that the initial and final state of charge of an energy storage system are equal. That constraint will ensure that an energy storage system can be used periodically and is given by ESS SOCiESS , k 1  SOCi , k T  .

3. Case study This case study considers a grid-connected multi-microgrid system, which consists of three microgrids. Each microgrid includes a number of generating units, storage units and a load demand as shown by Table 1 and Fig. 1. Each microgrid includes a 480 kWh lithium-ion battery with an initial state of charge of 70% and with a maximum and minimum state of charge of 100% and 10% [4], respectively. The upper and lower ramp rate limits of the microturbines are equal to 50% of their capacity [5]. The maximum amount of power that may flow between any microgrid and the main grid is 250 kW whereas the maximum amount of power that may flow between the microgrids is 100 kW. Table 1. Characteristics of the distributed generating units and storage units within each microgrid [6]. Generating/storage unit

Fuel cost ($/kWh)

Fuel cost ($/h)

Operational and maintenance cost ($/kWh)

Start-up and shutdown cost ($)

Lower limit of the output power (kW)

Upper limit of the output power (kW)

Photo-voltaic in MG 1

n/a

n/a

0.0048

n/a

0

60

Wind turbine in MG 1

n/a

n/a

0.0095

n/a

0

40

Battery in MG 1

n/a

n/a

0.0133

n/a

-30

30

Micro-turbine in MG 1

0.0475

0.9667

0.0079

0.1109

3

40

Photo-voltaic in MG 2

n/a

n/a

0.0048

n/a

0

30

Wind turbine in MG 2

n/a

n/a

0.0095

n/a

0

75

Battery in MG 2

n/a

n/a

0.0133

n/a

-30

30

Micro-turbine in MG 2

0.0412

1.1094

0.0063

0.1426

5

50

Photo-voltaic in MG 3

n/a

n/a

0.0048

n/a

0

45

Wind turbine in MG 3

n/a

n/a

0.0095

n/a

0

50

Battery in MG 3

n/a

n/a

0.0133

n/a

-30

30

Micro-turbine in MG 3

0.0475

0.9667

0.0079

0.1109

3

35

The mathematical terms within the first objective function can be derived through the use of Table 1 and Fig 2. Similarly, the mathematical terms within the second objective function can be derived through the use of Table 2.



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Fig. 1. Output power of the photo-voltaic system and wind turbine as well as the load demand within each microgrid [6], [7].

Fig. 2. Main grid and microgrid tariffs [6]. Table 2. Emission rates of various generating units [8]. Photo-voltaic system

Wind turbine

Micro-turbine

Main grid (coal power plant)

NOx (g/kWh)

0

0

0.1996

2.54

CO2 (g/kWh)

0

0

0.7239

959.35

SO2 (g/kWh)

0

0

0.0036

6.08

4. Results 4.1. Evaluating the performance of the proposed optimization model Performance of the proposed optimization model is evaluated by comparing its results to the results provided by an existing optimization model developed in [6]. The model in [6] is one of the best models for a multi-microgrid system; however, it does not minimize the emissions, neither does it consider the ramp rate limits of the controllable generating units. The existing and proposed models were applied twenty times on the case study and solved through the use of hybrid functions between Matlab’s genetic algorithm, fmincon and fgoalattain [9]. The twenty unique solutions provided by each model are used to calculate the average, median, minimum and maximum solutions. 4.2. Evaluation of the results Figure 3 indicates that the proposed model provided an average solution, which increases the operational cost by 19.4%; however, it decreases the emissions by 73.9%. There is an increase in the operational cost because during the off-peak period (21:00 to 6:00) the optimization model will try to minimize the operational cost by purchasing power from the main grid ($0.027/kWh) as it is less expensive in comparison to the fuel cost of the micro-turbines ($0.04/kWh). However, at the same time the optimization model will try to utilize the micro-turbines as much as

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possible to minimize the emissions. This trade-off between the micro-turbines and the main grid during the off-peak period causes the increase in the operational cost when trying to reduce the emissions.

Fig. 3. Results provided by the existing optimization model and proposed optimization model.

5. Conclusion Previous optimization models only minimized the operational cost of a multi-microgrid system. However, a new optimization model has been proposed, which not only minimizes the operational cost but also the emissions. The average solution provided by the proposed optimization model increases the operational cost by 19.4%; however, it decreases the emissions by 73.9%. This suggests that the proposed model should definitely be considered if an environmentally friendly energy management plan needs to be identified for a multi-microgrid system. Acknowledgements This work is supported by the Centre of New Energy Systems and the National Hub for the Postgraduate Programme in Energy Efficiency and Demand Side Management at the University of Pretoria. The work is also supported by the Royal Academy of Engineering under the Industry Academia Partnership Programme – 17/18. References [1] [2] [3] [4] [5] [6] [7] [8] [9]

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