1

Demand Response and Flexible Management to Improve Microgrids 1

Energy Efficiency with a High Share of Renewable Resources 2

Seyed Mehdi Hakimia, Amin Hajizadehb, Miadreza Shafie-khahc, João P. S. Catalãod,* 3

a Department of Electrical Engineering and Renewable Energy Research Center, Damavand Branch, Islamic Azad University, Damavand, Iran 4 b Department of Energy Technology, Aalborg University, Esbjerg, Denmark 5

c School of Technology and Innovations, University of Vaasa, Vaasa, Finland 6 d Faculty of Engineering of the University of Porto and INESC TEC, Porto, Portugal 7

8 * corresponding author: catalao@fe.up.pt 9

10

Abstract 11

Energy and social welfare management of smart buildings have been influenced by cooling systems. Although the 12

combination of cooling systems in the smart grid has stimulated serious discussions over the last decade, its execution 13

and control with more penetration of renewable energy have not been directly tackled. Hence, the present paper is 14

designed to explore the suitability of implementing a novel controller for a cooling system in smart grid settings and 15

high shares of renewable energies . The controller operates from a local control entity by responding to a set of inside 16

nominated points and outside signals, such as access to renewable energy sources and customer welfare. Not only it 17

reduces the purchasing power from the distribution grid with the help of optimization processes, but also minimizes 18

the overall cost and size of the microgrid. Managing the cooling system simultaneously increases the reliability of the 19

microgrid. As a result, the smart cooling system and renewable energy operate in unity, thus providing separate and 20

mutual benefits for the whole system. The results presented in this study support that the proposed cooling system 21

controller is capable of planning a microgrid system. 22

23

Keywords: Demand response; renewable resources; microgrid; smart building; active controller; optimization. 24

Nomenclature 25

Psurplus Surplus power

Psurplus,avg Daily average of the extra power signal

σsurplus,real The output energy flows

Tdesired The reference of preferred temperature (°C)

Tact The reference of actual temperature (°C)

Tmin Minimum actual temperature (°C)

Tmax Maximum actual temperature (°C )

2

Ramphigh Increasing rate between Tdesired and Tmax (°C -1)

Ramplow Decreasing rate between Tdesired and Tmin (°C -1)

σmin Standard variation of extra power at Tmin

σmax Standard variation of extra power at Tmax

PWT Generation of wind turbine

PPV Generation of photovoltaic power plant

Pbase Electrical consumption of microgrid

Q Heat transport from the air conditioner

Q Heat loss to the inside and outside environment

휌 Density of air

푉 Domestic area volume

Q Latent load (W)

CMH Infiltration airflow

U Thermal conduction coefficient (W/m2C)

A Structured surface (m2)

∆푇 Air temperature divergence indoors and outdoors (°C)

Pbase(i) Base load

Pcooling(i) Load of the cooling system

1- Introduction 1

Some power system operation methods manage the power generation based on the estimated network load. In rare 2

cases the loads are directly regulated, for instance when generation is not sufficient during peak days, the load would 3

be reduced via demand-side management algorithms. In general, the power generation is distributed to follow the 4

demand. Evidently, as additional renewable energy is added to the grid it becomes less and less easy to match the 5

network load. There is a lack of certainty when it comes to wind and solar power resources in microgrid. By generating 6

power intermittently, supplies are neither predictable nor possible to schedule. 7

The generation of power by means of solar sources varies because of changes in weather. The wind energy generation 8

profile also changes day by day showing rapid alterations. As additional un-dispatchable renewable power generation 9

is included, it will become ever more difficult for fast ramping; also load following and controllability of power 10

generation. Smart grids have the advantage of utilizing the direct control of loads. In this situation, loads can be made 11

more dispatchable in order to compensate for generation when it becomes less dispatchable. Dispatched demand and 12

3

demand-side management are alike since they both involve turning loads on and off. However, there is a difference 1

between the ways which they are applied. Demand response is used rarely and generally for load curtailment objectives 2

through periods of peak load, while demand dispatch is planned to be applied strongly at every period to contribute 3

keen on services that improve the action of the power system. It is referred to as demand dispatch, since in real-time 4

loads are dispatched. Power balance can be achieved by turning the load on or off, producing the similar outcome as 5

rising or decreasing the generation. The demand dispatch scheme aims to create generation-following loads. Currently, 6

the majority of grid dispatches generation utilizes this strategy. 7

Considering all facilities and electric appliances in a building, the cooling system consumes a significant share of the 8

total energy [1]. Therefore, a major contribution to maintain the power balance of the building is achieved by designing 9

proper demand dispatch of the cooling system. 10

In order to carry out the study, solar and wind power generation, fuel cell and electrolyzer with cooling systems are 11

incorporated in the simulated microgrid. The reason for considering cooling systems is that they are flexible loads 12

raising the possibility of following renewable energy generation, allowing the increase of the penetration of renewable 13

energy with minimizing the overall price of the microgrid. Some inhabited electricity consumption arrangement 14

problem by different electrical devices in smart grid has been described by [2]. A multi-residential electricity load 15

scheduling problem with multi-class appliances in the smart grid is studied. Through an optimization problem, the sum 16

of the overall satisfaction levels of residences is maximized. The objective function is defined as the sum of utilities of 17

the residential customers minus the total cost for energy consumption . However, the rapid responses of energy 18

management system in the grid emergency are not involved in this study. 19

The cooling system can control the set temperature [3]. Controlling the cooling system only in the demand response 20

time does not account for the economic benefits obtained from other times [4]. Further, a disadvantage is that the 21

thermal model is simple and not like the actual temperature change shape [5]. Strategies to minimize both the demand 22

cost and energy cost are different. Previous studies have adopted necessary strategies, but the overall process was 23

difficult to understand [6,7]. 24

In [8] it has been offered a consumer comfort model under the thought of consumer waiting times, which is a nonconvex 25

problem. Recently, in [9], a coupon incentive scheme was proposed to persuade small scale customers in reducing the 26

load in peak price period. 27

It is advised in [10] to utilize a new algorithm for intelligent load control and load curtailment to minimize energy not 28

supplied in impulsive load variation of grid and decrease the peak-to-average relation. The presented work by [11] 29

4

offers an effective attractive method contain communication-based demand response and a consumer-pleasant inclining 1

block tariff that consider consumers’ profits and consumption profiles. 2

On the other hand, in [12]–[15], multi-objective optimization of microgrids in which economic, environmental and 3

demand response issues are considered has been evaluated. In [12], a smart park microgrid which consider 4

photovoltaic panels. The obtained study in [13] develops a complete organization and investigation of demand 5

response’s obstacles and enablers in a smart grid framework. In [14], a stochastic programming method is planned to 6

optimize the performance of an intelligent microgrid to minimize operation prices and emissions by renewable 7

resources In [15] the result of a novel scheme architecture and control algorithm that is able to apply together battery 8

storage and control the temperature of thermal domestic devices is presented. 9

However, they have attempted to model the residential energy demands by means of pre-specified behavioral rules. 10

While there are some uncertainties in load demand, weather conditions and energy storage’s state of charge. Therefore, 11

researchers have recently achieved to formulate optimization problem of residential A/C control. For instance, in their 12

ongoing investigations, they have tried to limit the combination of thermal discomfort and energy consumption under 13

variable electricity price [17]–[20]. 14

Kondziella et al. [21] aim to classify the technical approaches that have been used in elasticity demand studies. Ref 15

[22] describes a DR development model for the new housing society incorporates the present conditions and the future 16

styles of DR agenda. Ref [23] concentrates on the two-way communication linking distribution grid and several 17

inhabited customers, and every consumer is considered as a smart house energy management structure. In [24] an 18

original incorporated form of demand in the presence of energy efficiency programs in addition to DR plans has been 19

designated. 20

In [25] presents a DR method for energy management in a hierarchical power market that believes both the service 21

provider’s income and customers’ expenses. A novel model to determine a digit to decide the number of chillers/pumps 22

to be shut down, also how to adjust a load of retained equipment systematically through demand response procedures 23

is described by [26]. A method is expanded by [27] to carry out the demand response for domestics in smart grids, 24

which consider PV generation and energy storage. 25

In [28] an original informational systematic demand response management method for an inhabited customer with a 26

goal to decrease the peak load is presented. In [29], a realistic pricing method is planned to persuade various customers 27

to contribute to demand response by supplying them with a list of price plans. Ref [30] presents a new control algorithm 28

for joint demand response management and thermal comfort optimization in microgrids equipped with renewable 29

energy sources and energy storage units. Ref [31] proposes a simulation-based optimization method for the scheme of 30

5

an energy management system in grid-connected photovoltaic-equipped microgrids with heterogeneous occupancy 1

schedule. Ref [32] presents an original control algorithm for joint energy demand and thermal comfort optimization in 2

photovoltaic-equipped interconnected microgrids. Ref [33] presents a home energy management model consisting of 3

microgrid framework and demand side management (DSM) technique. To reduce peak load, peak to average, and 4

energy cost, households’ loads were shifted on the basis of price-based tariff such as flexible and time of use tariff. 5

In Ref [34], an integrated planning model was developed to investigate the techno-economic performances of a high 6

renewable energy-based standalone microgrid. Ref [35] presents a suite of demand response constraints that capture 7

more-realistic demand response operational limitations including uptimes and downtimes, numbers of starts per day, 8

allowable power limits, and required recovery periods. Ref [36] presents novel methods for Demand Response 9

programs in order to deal with operational uncertainties, such as wind energy and energy price of upstream network, 10

within the framework of a smart microgrid. Ref [37] proposes a novel energy management system for an isolated 11

structure of networked microgrids. In Ref [38], an innovative model is provided as a strategic management. It is 12

intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, 13

storages, and un/controllable loads. The contribution of Ref [39] is to propose a way to facilitate and assess renewable 14

sources’ integration into manufacturing systems, by exploring an optimization model that obtains a production schedule 15

adapted to match the onsite renewable energy supply, with energy storage systems and the power grid as backups. 16

Ref [40] recommends chaotic fast convergence evolutionary programming rooted in Tent equation to solve 17

hydrothermal generation scheduling incorporating pumped-storage-hydraulic unit with demand side management 18

considering uncertainty and outage of renewable energy sources. Ref [41] proposes a hierarchical demand response 19

scheme, which is based on a new two-level market mechanism that results in a win-win situation for both parties. Ref 20

[42] proposes the demand-side power procurement problem to optimally reduce consumer's energy cost. The 21

motivation stems from pressing issues on an increase of energy cost in an industrial section. In Ref [43], a peer-to-peer 22

energy trading platform among residential houses is proposed to coordinate demand response schemes and level of 23

potential generation/consumption disturbances in the hour-ahead intraday context. 24

According to the previous literature review, the main novelties of the proposed paper are: 25

- A new method is developed for demand-side management in smart grids considering high penetration of 26

intermittent renewable energy generation sources; 27

- Making the house demand flexible to follow the variable power output of renewable generation resources and 28

matching the response behavior of generation and consumption in smart grids with high penetration of 29

intermittent renewable generation sources; 30

6

- Long term planning of smart grids considering high penetration of intermittent renewable energy generation 1

sources and taking into account the demand side management of smart buildings. 2

3

2- The Active controller 4

One of the methods of controlling ventilation loads is using active controllers. Active controllers set the thermal load 5

based on the set-point of consumer and the external signal in such a way that consumers’ welfare is not disturbed, and 6

the consumption is reduced at the needed time. In this paper, the external signal is considered as the difference between 7

the generation of renewable energy resources and the MG’s loads. In fact, these types of controllers transform the 8

amount of consumer thermal comfort into a mathematical model and compare it with the external signal to decide 9

whether to warm or cool the place [15, 27]. 10

The power consumption of the cooling system is adjusted according to the environment’s temperature so that the room’s 11

temperature reaches to this point, which is known as the actual temperature. This adjustment should be made in such a 12

way that firstly it does not disturb consumer’s comfort, secondly, it reduces consumption when the difference between 13

the production of renewable energy resources and the MG consumption is low. A home energy management system 14

can be used to implement this method [15, 27]. 15

In a microgrid, local control determines the surplus of power, Psurplus, which is transmitted to all active controllers 16

through a communication system. The surplus power is the outcome of difference between renewable production and 17

demand. Home Energy Management (HEM) system receives the surplus power signal, while the information is 18

distributed among the active controller in the building. The accessibility of renewable resources in a specific period is 19

indicated by the surplus power signal [15, 27]. 20

Together with the surplus power signal; the daily average of the extra power signal, Psurplus,avg, and a daily standard 21

deviation of extra power, σsurplus,real are relayed to the active controllers. Including all three signals, the basis is provided 22

to evaluate the power signals among the past 24 hours. An active controller is required in place for the domestic 23

demand to make a response to the received signals [15, 27]. Although active controllers could manage more end-user 24

demands, a cooling system has been selected for further investigation of residential appliances. Here, the implemented 25

cooling system’s active controller employs the daily standard deviation of extra power, σsurplus,real considering the below 26

parameters: 27

The block diagram of the active controller is shown in Fig. 1 28

7

1

Figure1. The block diagram of the active controller 2

Where PREN is the generation of renewable energy resources and Pload is the microgrid's loads. 3

2.1. The Cooling model 4

푃 (푡) = 푃 (푡)− 푃 (푡) (1) 5

푃 (푡) = 푃 (푡) + 푃 (2) 6

Where, PWT is considered as the generation of wind turbine, PPV is the generation of photovoltaic power plant, and Pbase 7

shows the consumption of MG. 8

In this study, Psurplus, and Psurplus,avg are replaced by 휎 , . 9

휎 , =푃 − 푃 ,

푃 , (3) 10

푃 , =∑ 푃 ,

24 (4) 11

The controller receives additional information from extra power signal, Psurplus, and, Psurplus,avg. Whenever the extra 12

power signal is equivalent to Psurplus,avg, the active controller sets Tact, to Tdesired. However, when Psurplus and Psurplus,avg 13

are not equal, Tact is moved away from the Tdesired. As long as Psurplus is lower than Psurplus,avg, the controller places the 14

Tact, higher than Tdesired. The maximum allowable temperature does not go beyond Tmax. Provided that Psurplus rises above 15

Psurplus,avg, the controller places Tact lower than Tmin. ΔT represents the difference between Tdesired and Tact. A positive 16

value for ΔT (ΔT>0) signifies higher Tact compared to Tdesired. While Psurplus is lower than Psurplus,avg , the Tdesired increases 17

conversely. Another parameter that has significant effect on the active controller is σsurplus,real. It depends on the 18

available Psurplus and Psurplus,avg. Fig.2 illustrates the active controller performance which presents the relation between 19

σsurplus,real and the temperature reference values. 20

8

1 Figure2. Active controller performance for a specific cooling system 2

3 Figure 2 shows the relation between σsurplus,real and set-point of active controller. The main purpose of this figure is the 4

temperature adjustability. 5

It is clearly obtained from Fig.2,that Rlow and Rhigh may get different values, pointing out various consumers preferences 6

in response to heating and cooling habitual. In other words, end-users may have the tendency to increase the house 7

temperature above Tdesired, thus far be resistant to allow decreasing temperature below the heating temperature reference. 8

Also, it is not necessary for Tmin and Tmax to be symmetric around Tdesired. 9

Equation (5) calculates the actual temperature reference of the active controller which is subject to the constraints of 10

equation (6). Equations (7-8) determine Rlow, Rhigh. 11

푇 = 푇 + 훥푇 (5)

푇 − 푇 ≤ ∆푇 ≤ 푇 − 푇 (6)

푅 = , 푖푓 휎 , > 0 (7)

푅 = , 푖푓 휎 , < 0 (8)

In order to calculate ΔT, it requires ascertaining whether Psurplus is higher or lower than Psurplus,avg to begin with. This 12

could be worked out by assigning 휎 , (equation 3). For an inhabited application, positive values indicate that 13

the Psurplus is lower than the Psurplus,avg and negative ones indicate that it is higher. The value of ΔT where 휎 , >0 14

is given by equation (9) and 휎 , <0 is specified by equation (10), supposing zero value 휎 , no ΔT is 15

generated. 16

∆푇 = , 휎 , > 0 (9)

9

∆푇 = , 휎 , < 0 (10)

Merging equations (5), (9), and (10), equations (11) and (12) are obtained which are nevertheless subject to the 1

constraints determined by equation (6). Provided that Psurplus > Psurplus,avg, yields a 휎 , l>0, the Tact is born by 2

equation (11) furthermore, if Psurplus < Psurplus,avg, yields a 휎 , <0, and is computed by equation (12). 3

푇 = 푇 + , 휎 , > 0 (11)

푇 = 푇 + , 휎 , < 0 (12)

It is important to note that based on Figure 2 and equations (11) and (12) no absolute Psurplus or temperature reference 4

values are available. The Tact is tuned based on 휎 , and internal reference values by the active controller. It is 5

essential for the device to have the flexibility and be capable of operating at different Psurplus levels. 6

7 3. Determining the cooling system power consumption 8

Regarding the technique explained in Section 2, the variation of indoor temperature, which should be provided by the 9

cooling system, is computed as follows: 10

∆푇 = 푇 − 푇 (13)

Equation (14) must be satisfied to guarantee that the deviation of the pointed temperature is a proper value. 11

푄 −푄 = −푚. 푐 .∆푇 (14)

That Q is the heat transport from the air conditioner. Q is the heat loss to the inside and outside environment 12

throughout the barrier, glass and roof area and summation of inside demands. Equation (14) is reworked as bellow: 13

푄 −푄 = −휌 .푉 . 푐 .∆푇 (15)

Where 휌 is the density of air and 푉 is the domestic area volume. 14

In many cases, the cooling system’s efficiency is related by the Seasonal Energy Efficiency Ratio (SEER). [44]. 15

Equation (15) could be defined as below: 16

푃 × 푡 × 푆퐸퐸푅 − 푄 = −휌 .푉 . 푐 .∆푇 (16)

10

푃 ≤ 푃 ≤ 푃 (17)

3.1. Heat loss for the cooling system 1

for the purpose of evaluating the cooling demand, the parameters below should be taken into account [7]: 2

Heat gain from external loads: sun, exterior walls, roof, glasses, 3

Heat gain from internal loads: ventilation or infiltration, interior partitions, equipment, light fixtures and 4

residents. 5

Cooling consumption load is comprised of radiation, conduction, convection, and interior demands. Cooling data is 6

used as a substitute for heating to calculate conduction load. The air infiltrated latent load by the formula below [7] and 7

responsible load as heating, make up the convection load. 8

푄 = 퐶푀퐻 × 0.82 × ∆푊 (18)

Where Q latent load (W), CMH (Cubic Meter per Hour) is infiltration airflow (m3/hour). Transferred thermal 9

demand is computed by equation (19) [7]: 10

푄 = 푈 × 퐴 × ∆푇 (19)

Where U is the thermal conduction coefficient (W/m2C), A is the structured surface (m2) and ∆푇 is the Air temperature 11

divergence indoors and outdoors (°C). 12

The thermal conduction coefficient (U) is computed as presented below: 13

푈 = ∑ (20)

푅 = 풙풊풌풊

(21)

Convection demand could be determined as below [7]: 14

푄 = 0.335 × 퐶푀퐻 × ∆푇 (22)

That 0.335 is the sensible thermal factor, CMH is the airflow infiltration rate (m3/hour) and ∆푇 is the Air temperature 15

variation between indoors and outdoors (°C). Thus, 푄 is given as: 16

푄 = 푄 + 푄 + 푄 (23)

Figure 3 provides a flowchart of the recommended method. 17

11

1 Figure3. Flowchart of the proposed method 2

4. Simulation results 3

Thus far, the peak demand of the microgrid is considered 500kW i.e. the total demand regardless of the cooling load. 4 The amount of every wind power generation system and the rest of the unit i.e. Photovoltaic, Fuel cell, electrolyzer, 5 and hydrogen tank are 7.5kW and 1kW in that order. The optimal sizing of the components which form the microgrid 6 is established by the proper management of cooling loads. Indoor cooling is provided by means of air conditioning. It 7 has been attempted, in this study, to manage the cooling loads on the basis of an intermittent renewable generation with 8 wind, solar, microgrid load, the outdoor temperature and the preferred indoor temperature of the end-users. The main 9 aim has been to look into the influence of the cooling managing system on the optimal sizing of microgrid elements, 10 the reliability of demands and the overall cost of the microgrid. More detail of economic data is shown in Table 1 [45-11 48]. 12

Table 1. The economic data of each component. 13

Wind PV Fuel Cell Electrolyzer Hydrogen Tank Capital cost($) 1850 1300 1500 500 250 Replacement

cost($) 1400 600 800 55 40

Operation and Maintenance

cost($/kw/year)

85 40 150 50 45

12

In Figure 3, first, Psurplus,avg and σsurplus,real are calculated based on Eq. 3 and Eq. 4, then Rhigh and Rlow are calculated 1

by Eq. 7 and Eq.8. The real temperature is determined by Eq. 11 and Eq. 12 considering constraint in Eq. 6. At the next 2

step, the power consumption of cooling system is calculated by Eq. 16. The minimum and maximum acceptable power 3

of cooling system are checked, and if this limit is not met, the power of cooling system is defined in permissible range. 4

For example, if the power is higher than PACmax, it will be set on PACmax, and the real temperature will be recalculated. 5

Optimal sizing of the microgrid components can be achieved by determining the following parameters as inputs to the 6

program: 7

- Microgrid components: solar and wind resources. 8

- Energy storage components. 9

- Wind and solar production’s specification 10

- Outside temperature 11

- Desired temperature: Tmax and Tmin 12

- Energy purchasing price: from and into the distribution grid. 13

- Hourly load curve. 14

- A typical air conditioner’s lowest and highest power usage 15

- The essential information for Particle Swarm Optimization (PSO). 16

- Mentioned building’s features: walls, windows, etc. 17

In the simulation process, a transformer with 90% efficiency and 100kVA capacity is considered. The tariffs on 18

electricity in this paper are based on case studies in Iran. The electrical energy rates are presented in table 2. 19

Table2. The electrical tariffs for inhabited properties in Iran within the consumption range of over 30kW and under 20

30kW 21

Middle load Peak load Off-peak load

Hour 07:00-19:00 19:00-23:00 23:00-07:00 $/kWh (the power more

than 30kW) 0.034 0.068 0.017

$/kWh (the power less than 30kW) 0.044 0.088 0.022

22

13

The assumed house size for a cooling system’s load control method to be implemented and installed was 160m2 with 1

10m×16m in dimensions. Side walls may have accrued the heat interchange i.e. (50m2+50m2+28m2+28m2). The k 2

factor of the window equals to 0.05 (w/m.k) according to the computation. The walls are 30cm in thickness. Therefore, 3

two hundred cooling systems were estimated to be used in the simulated microgrid to keep the inside temperature 4

around 23°C, and the power consumption is displayed in figures 4 and 5. As can be seen in Figures 5 and 6, when the 5

outside temperatures rise, the energy consumption increases as well. 6

7

Figure4. Hourly power consumption of cooling systems without control 8

9

10

Figure5. Hourly power consumption of cooling systems without control during 10 days 11

14

1

Figure6. The hourly outdoor temperature during 10 days 2

3

The optimal number of microgrid components prior to organizing the energy usage of the air conditioning system are 4

given in table 3. Table 4 depicts the optimal quantity and price of buying energy from the distribution grid on the one 5

hand and, the optimal quantity and price of sold energy to the distribution grid on the other, in addition to the optimal 6

price of the microgrid, shed loads, punishment for interrupted loads and microgrid reliability. In this part the impact of 7

cooling load managing on optimal sizing of microgrid components has been considered. Figure 7 shows the comparison 8

between optimal sizing of microgrid components before and after cooling system management. 9

It is necessary to determine the following parameters before employing the presented control methodology: 10

- The preferred temperature in warm time of year, i.e., summer: 23°C 11

- The lowest and highest temperature in hot time of year: 19-26°C 12

13

Table3. Optimal price and amount of every component in microgrid in the absence of cooling system 14

management. 15

Total cost ($) Transformer (KVA) Fuel Cell Hydrogen

tank Electrolyzer Wind turbine Photovoltaic

1.96361107 57 522 4397 875 202 598 16

15

1

Figure7. Optimal sizing of microgrid components without and with cooling management 2

Table4. The quantity and price of exchanging energy, interrupted loads and penalty in the absence of cooling system 3

management 4

ELF Penalty for

Interrupted load ($)

Interrupted load (kWh)

Cost of selling

energy ($)

Selling energy (kWh)

Cost of buying

energy ($)

Buying energy (kWh)

0.00253 8.4508105 15.3702103 1.052105 1.0716105 2.194104 2.234104 5

Figures 8-12 show, the divergence between power production of renewable energy and microgrid load, the actual inside 6

and outside temperature in a 10-day period, the controlled power consumption of cooling systems throughout a year 7

and also in a 10-day period. 8

9

Figure 8. Hourly power consumption of cooling systems with active controller 10

0

1000

2000

3000

4000

5000

6000Si

ze

Whithout cooling management

After cooling management

16

1

Figure 9. Power consumption of cooling systems with active controller in 10 days 2

3

Figure 10. variation between power production of renewable energy and microgrid demand in 10 days 4

5

17

1

Figure 11. Actual temperature of inside in 10 days 2

3

4

Figure 12. The outside temperature in 10 days 5

6

As it is apparent in figures (8-12), the quantity of electricity usage of cooling systems is relative to the extra power and 7

outside temperature. The indoor temperature of the house gets adjusted as stated by the extra power. For instance, the 8

temperature can be lowered when the surplus power is low. Based on the amount of surplus electrical power the inside 9

temperature is adjusted. 10

18

As it can be observed, Table 5 provides the optimal number of microgrid components past organization the usage of 1

cooling systems. Moreover, Table 6 depicts the optimal quantity of exchanged power from/to distribution grid, the 2

loads which were not supplied due to related expenses and microgrid reliability. 3

Table 5. Optimal price and amount of power consumption in each microgrid unit in the presence of the cooling 4

system management 5

Total cost ($) Transformer (KVA) Fuel Cell Hydrogen

tank Electrolyzer Wind Turbine Photovoltaic

1.8009107 90 521 4764 675 158 422 6

Tables 3 and 5 have been compared, indicating that the proposed method managed to reduce the size of renewable 7

resources and enlarge the quantity of hydrogen reserved in the tank. The evaluation of tables 4 and 6 shows that the 8

proposed technique has resulted in the lessening of the buying energy from the distribution grid and rising the power 9

sold to the distribution grid, in addition to, lessening load shedding and purchased gas from the grid. 10

Table 6. The quantity and price of exchanging energy, non-supply loads and punishment in the presence of the 11

cooling system management. 12

ELF Penalty for interrupted loads ($)

Interrupted loads (kWh)

Cost of selling energy

($)

selling energy (kWh)

Cost of buying

energy ($)

Buying energy (kWh)

0.000161 39765 903 1.386105 1.423105 6.464103 6.62103 13

In order to verify the effectiveness of the proposed PSO methodology, GA algorithm is also implemented to determine 14

the optimal design of the microgrid. The optimization results are shown in Table 7. It is obvious from Table 7, the total 15

cost and optimal sizing of microgrid components of the PSO algorithm is better than GA algorithm, which depicts that 16

the convergence speed and global search ability of PSO algorithm are better than GA algorithm. 17

Table 7. Simulation results comparison of PSO and GA algorithms. 18

Total cost($) Fuel Cell Hydrogen Tank

Electrolyzer Wind Turbine

Photovoltaic

PSO 1.8009×107 521 4764 675 158 422

GA 1.8347×107 528 4778 686 162 433

19

19

Next, the correlation of the cooling system demand before and after the use of the demand dispatch technique by the 1

energy surplus curve has been studied to confirm the offered cooling system managing technique. The correlation 2

values between Psurplus , Pcooler,Unmannaged and Psurplus , Pcooler,Mannaged are 0.49 and 0.78 correspondingly. The changing 3

rate of the cooling system consumption gets nearer to the changing rate of the extra power generated by renewable 4

energy, so as to expand the ability for higher renewable resource penetration. 5

Figure 13 shows the cooling system power usage before and after load management and renewable energy generation 6

power surplus. It can be seen that when the surplus power is low (i.e. the load is higher than the renewable energy 7

generation), the consumption is reduced because of the way the controller operates. 8

9

Figure13. The extra power of renewable resources and the cooling system consumption 10

11

The whole load is composed of two items: 12

1. The base load Pbase(i) representing total demand inside a microgrid excluding cooling loads. 13

2. Pcooling(i) representing the load of the cooling system. 14

15

The measurement was taken at hourly intervals denoted by i. It can be gathered that the base load Pbase(i) remains 16

steady in the measured period i. 17

푃푙표푎푑(푖) = 푃푏푎푠푒(푖) + 푃푐표표푙푖푛푔(푖)

20

(24) 푃푙표푎푑 (푖) = 푃푏푎푠푒(푖) + 푃푐표표푙푖푛푔 (푖)

(25)

푃푙표푎푑 (푖) = 푃푏푎푠푒(푖) + 푃푐표표푙푖푛푔 (푖)

Regarding the obtained results, the correlation values between Psurplus , Pload,old and Psurplus , Pload, new are 0.5 and 0.72 1

correspondingly. 2

Where Pload represents the cooling load value before considering the presented demand dispatch technique and 3

Pload denotes the cooling load after being employed. The introduced method develops a positive correlation 4

between demand of microgrid and the extra power by renewable resources. 5

6

5. Conclusion 7

The present investigation intended to introduce an original smart cooling system for household purposes. The purpose 8

was to examine a viable implementation of an active controller. A mixture of inside settings and outside signals from 9

a local control unit actuated the proposed active controller. As a result, the cooling management system was optimized 10

by minimizing the price of the microgrid, minimizing the amount of renewable resources and importing power from 11

the grid. The overall reliability of the microgrid was also improved. The proposed method and its simulation results 12

suggest that managing the consumption of cooling systems would reduce the amount of renewable resources while the 13

quantity of hydrogen in the hydrogen tank would increase. This would successively increase the sold power to the grid 14

and lower the rate of purchased power from the distribution grid, non-supply loads, followed up by a decrease in the 15

whole expense of the smart microgrid. The outcomes confirmed that the intelligent cooling systems and sustainable 16

resources could function effectively combined with their benefits added together. 17

Acknowledgment 18

S.M. Hakimi would like to thank the research council of Islamic Azad University, Damavand, Iran for 19

financial support of this research project. J.P.S. Catalão acknowledges the support by FEDER funds through 20

COMPETE 2020 and by Portuguese funds through FCT, under POCI-01-0145-FEDER-029803 21

(02/SAICT/2017). 22

23

21

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