Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

Nikkhah, Mohammad Hasan; Lotfi, Hossein; Samadi, Mahdi; Hajiabadi, Mohammad Ebrahim

doi:https://doi.org/10.1155/2023/9042957

International Transactions on Electrical Energy Systems

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Abstract Introduction Conclusion Data Availability Conflicts of Interest References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 9042957 | https://doi.org/10.1155/2023/9042957

Energy Hub Management considering Demand Response, Distributed Generation, and Electric Vehicle Charging Station

Mohammad Hasan Nikkhah,¹Hossein Lotfi,¹Mahdi Samadi,¹and Mohammad Ebrahim Hajiabadi¹

Academic Editor: Davide Falabretti

Received12 Apr 2023

Revised09 Jun 2023

Accepted04 Jul 2023

Published14 Jul 2023

Abstract

Multicarrier energy systems have systematic flexibility for energy and load management. The integration of different types of energy under the title “energy hub” (EH) in multiple energy infrastructures such as electricity, natural gas, and heat in an integrated manner will meet demand at a lower cost. On the other hand, the desire for distributed generation (DG) systems has increased due to their many economic and technical advantages, as well as the technical development of these units and the reduction of environmental pollution. In this paper, a proposed EH system is optimized in terms of cost using linear programming, considering transformer converters, combined heat and power (CHP), heat exchangers, and energy storage, including electric vehicle charging stations (EVCSs) and heat storage. In this regard, four proposed scenarios are defined and evaluated from the aspect of cost in order to mitigate load shedding while considering uncertainty in order to bring the problem closer to reality. The results show that the total cost in scenarios 1, 2, and 3 (the best case) is reduced by 15.47%, 26.29%, and 27.93% compared to Scenario 0 (the base case), and Scenario 3 is the most economical possible case. This method can be a suitable tool for energy hub management, especially for solving possible load shedding.

1. Introduction

1.1. Motivation

A recently created idea called an energy hub (EH) is used as an interface between various energy infrastructures and network users, and it is thought to be a viable choice for the best management of multienergy systems (MESs) [1]. Through modern technological advancements such as energy storage (ES) systems and energy converters, this conceptual idea has been used to describe the interactions between various energy carriers. By arranging converters and storage systems optimally, which increases flexibility in terms of fulfilling the various forms of projected demand, it is possible to balance the supply and demand of different types of energy [2]. Large building complexes (such as malls, hospitals, and airports), metropolitan regions, tiny confined districts, island power systems (ships, trains), and industrial units like steelworks are some examples of practical EH application areas [3]. Numerous studies have thus far highlighted various facets of MES and EH, including security and dependability [4–6], operational optimization [7–9], and long- and mid-term planning [10, 11]. Demand response (DR) is one part of energy management that has been conceived in recent years when the EH is employed. DR is a very important parameter that can work in concert to improve the effectiveness and functioning of electrical energy systems [12]. A certain amount of flexibility in serving the loads is provided by redundant channels within the hub. When dealing with DR schemes, this supply redundancy can be a useful tool for customers. For instance, the EH can easily stop buying electricity when the incentives for electric load curtailments are high and switch the primary source of electric energy to other energy carriers, such as natural gas, by using CHP to make up for the input electric energy that was not purchased. As a result, the customer buys low-cost gas to cover the necessary electric energy while receiving a larger incentive because the required electric energy was not purchased. In this regard, the aim of this paper is to manage EH (with the presence of energy converters and ES) by considering the electric vehicle charging station (EVCS), the DR program, and distributed generation (DG) simultaneously. Also, the blackout program is included in the objective function of this problem, which is used to solve the blackouts created by the two tools of the DR program and DG.

1.2. Literature Review

Several studies have pointed to different aspects of EH use, such as security and reliability [4–6], operational optimization [8, 9], and long-term and medium-term planning [10, 13]. The optimal performance of energy hubs is modeled as a mixed-integer linear programming optimization problem [14]. Also, a least-cost paradigm is implemented into the model to fulfil the needs of industrial, commercial, and residential energy centers. Multicarrier energy systems are reviewed from different perspectives, including mathematical models, integrated components and technologies, uncertainty management, planning objectives, environmental pollution, flexibility, and robustness [15]. Considering the uncertainty in this study, the working point is closer to the optimal point. The nonlinear problem of energy management in interconnected EHs is solved using goal programming [16]. The analyst can determine the distributed energy hubs’ ideal operating conditions by using this solution technique. The energy interaction of hubs is formulated using game theory, considering the uncertainty in demands [17]. Researchers may examine the presence and distinctiveness of the Nash equilibrium using the potential game technique as well as create an online distributed algorithm to reach that equilibrium. A generalized heuristic method is proposed to solve the problem of optimal power flow in EHs [18]. This method does not need any additional variables, such as dispatch factors or dummy variables, which are necessary for traditional methods. All evolutionary algorithms can be used with the unified suggested strategy. A mathematical model for residential EHs is presented in which automatic decision-making technology is incorporated into the smart grid [7]. A two-level stochastic model is presented, where the customer is placed at the lower level and the center manager is placed at the upper level [19]. The performance of existing ES systems and multienergy systems has been investigated and compared, and the results show the superiority of multienergy systems [20]. The above papers have not considered factors such as DR, DG, and electric vehicle charging station (EVCS) in the problem of hub energy, which is the purpose of our paper to consider these issues.

A new steady-state method is presented for optimal energy management of EHs that helps overcome the limitations of the dominant EH management models [21]. A centralized operation framework is developed with the goal of minimizing system operating costs while addressing EH constraints [22]. The suggested methodology enables the microgrid operator to reduce microgrid operation costs while ensuring that certain operational restrictions on the EH are met. The optimal energy planning of multiple hubs with multiple energy infrastructures is studied [11]. The model also takes emission reduction incentive programs into account when installing distributed generation. Studying the design of future energy networks with connected energy hubs can be aided by this concept. A particle swarm optimization is implemented to solve the energy operation problem of residential hubs [23]. Some studies are presented on the design of a new approach for exploiting the energy of residential hubs in the electricity market [24]. The mentioned papers have only investigated the DG and DR factors and have not considered the EVCS factor. In addition, the robust planning of the combination of cooling, heat, and power EHs in the presence of demand uncertainty, photovoltaic systems, and wind power generation, as well as the price of electricity, has been investigated [25–27]. Reference [28] presents a two-level structure for the best EH operation and planning based on demand unpredictability and renewable energy sources. At the primary level, stochastic-probability models are used to show optimal planning, while at the secondary level, stochastic-probability models are used to present optimal operation. The proposed technique for problem-solving in continuous and discrete space is the foundation for the method. In order to manage the electricity and natural gas networks in an economical manner, reference [29] suggests a novel paradigm. In this model, an energy hub supplies electric and thermal loads by using water, electricity, and gasoline oil as inputs. In addition, the energy hub produces natural gas using power-to-gas technology and sells it to a gas network to lower costs and reduce congestion in gas pipelines. This model also incorporates a demand response program to move loads from peak to off-peak hours. A strong self-scheduling of a virtual energy hub for participation in the energy and reserve markets is proposed in [30]. It is suggested that the thermal reserve market be used to preserve the thermal power balance in real time and account for the consequences of thermal demand unpredictability. In [31], a brand-new EH operating model is put forth, using which the best responsive load changes may be made in relation to the calculated EH output energy prices as well as the EH schedules. A three-step strategy is suggested to reach this objective. Reference [32] develops a model-free, safe deep reinforcement learning approach for the best control of a renewable-based energy hub that operates on multiple energy carriers while satisfying the physical restrictions in the energy hub operation model. By taking into account numerous energy components, this work’s major goal is to reduce the system’s energy costs and carbon emissions.

The EH concept has also been used for other purposes in the power system and energy delivery infrastructure, such as its application in DR. An EH system, including a wind turbine, photovoltaics, and electric vehicles, which are exchanged with the energy, thermal, and gas sectors, the DR program, and the energy storage market, is proposed. Therefore, the uncertainty of wind turbine, photovoltaic, load, and electricity market prices is modeled by the Monte Carlo method [33]. An energy optimization management method for the interaction between the smart EH and users in the energy system is proposed [34]. The outcomes demonstrate that the technique is applicable for efficient energy management in an integrated energy system. A new EH operation optimization model is proposed [35], which determines the optimal response of responsive loads to the associated hub energy generation costs to minimize the total EH operation cost as well as customer payments. Various research studies have been conducted to date on the concept of integrated DR. The changes in energy consumption are examined through smart energy hubs combining electricity and natural gas [36]. In this study, customer payments are minimized while service profits are maximized. By proposing a new perspective on DR programs in the field of multienergy systems, a systematic review of DR programs is presented in multienergy systems to thoroughly examine the basic concept and value of integrated DR programs [37]. A residential EH is proposed where the customer’s benefit is maximized due to optimal load management considering movable and limited loads [38]. An optimization problem is developed to minimize the total energy cost of a residential EH operation while optimally scheduling home appliances in time-based DR programs [9]. The integrated DR concept is developed to model optimal interactions between smart energy poles and utility companies [39]. An integrated demand-side management-based (IDSM) contemporary energy management approach for electricity and natural gas networks is presented [40]. In traditional research, energy usage is optimized from the viewpoint of each individual user without taking into account how they interact with one another. The investigation of the energy hub system’s emission issue is found [41]. The cost-environmental functioning of the energy hub system in the presence of the demand response program (DRP) has therefore been presented using a multiobjective optimization model. Additionally, a mixed-integer linear programming method has been employed to analyze the energy hub system’s cost-environmental performance issue. A stochastic model for real-time electricity, natural gas, and demand is presented [42]. A smart energy hub works to reduce a weighted sum function made up of the cost of energy and the penalty for emissions. When electricity and natural gas are transformed into electrical, heating, and cooling energy at an intelligent energy hub’s output port, researchers apply the conditional value at risk (CVaR) approach to control the operational risk in order to have a more exact model. These papers only examined the DR factor and did not analyze the effects of the DG and EVCS factors.

1.3. Contribution

As mentioned, the impact of the factors EVCS, DRP, and DG on the energy hub has been investigated in a few articles. In this regard, the contribution of this paper, as shown in Figure 1, is as follows:(i)Investigate the effect of the DR program and DG on EH costs while considering EVCS simultaneously(ii)Consider energy storage (EVCS and heat storage)(iii)Consider DG in the electric load section and the DR program to solve the blackouts(iv)Consider the uncertainty of DG’s capacity to bring the problem closer to reality(v)Consider the entire state space to reach the optimal solution, unlike smart methods

The internal components of the energy hub under study are three converters: a transformer, a combination of heat and power, and a heat exchanger. Also, two electric and thermal storages are used to respond to the load in peak load conditions, which are usually charged in low load conditions and discharged in peak load conditions.

1.4. Paper Organization

The whole paper is divided into four sections. The basic and proposed formulas are presented in Section 2. In addition, the results of the proposed formulas are examined in Section 3. Finally, the conclusion of the paper is presented in Section 4. In the following, the formulas related to the problem will be presented.

2. Problem Formulation

In this section, the basic and proposed formulas related to EH are presented. First, the mathematical formulation of linear optimization is presented to find the optimal point by considering equality and inequality constraints. Then, the objective function of the problem is proposed according to the linear formulation.

2.1. Linear Optimization Formulation

In general, the mathematical formulation for a linear problem considering equality and inequality constraints is as in the following equation [43]:where symbol specifies the type of constraint and is defined as follows:(i)=: equality constraint(ii): greater than or equal to constraint(iii): less than or equal to constraint

2.2. Objective Function

The objective function of the optimization problem is the EH cost function. The objective function of the energy hub, with the presence of energy input carriers (electricity, gas, and heat) and output loads (electricity and heat) for a period of 24 hours a day, is as follows:where λ_e (t), , and λ_h (t) are the prices of electricity, gas, and heat carriers in hour t, respectively. Up_e (t) and Up_h (t) are equal to unsupplied electric power at hour t and unsupplied thermal power at hour t, respectively. In addition, the generation power of electricity, gas, and heat energy carriers in hour t is introduced by P_e (t), , and P_h (t). Also, the amount of penalty for unsupplied power is indicated by Pen. Furthermore, the limitations related to energy carriers are as follows:where P_e^Min (t) and P_e^Max (t) are, respectively, the minimum and maximum generation power of the electrical carrier at hour t. Similarly, it is the same for gas and heat carriers. In general, the relationship between the generation power of energy carriers (P (t)) and the output load (L (t)) with the presence of energy storage at the input (M (t)) and output (Q (t))of the hub is defined as follows [3]:

2.3. Formulation of the Basic Case

In this paper, the energy storage devices are placed at the outlet of the hub. Since all the storage devices are located in the output part of the energy hub and no storage is considered in the input part, M (t) is equal to zero. In this regard, (3) is rewritten as follows:where Q (t) is the rate of charging or discharging of the energy storage in hour t, which is defined as follows:where C_Q^ch (t) and C_Q^dis (t) are the matrix of charging and discharging coefficients of energy storage devices, respectively. Also, Q^ch (t) and Q^dis (t) are the charging and discharging rates of energy storage devices in hour t. Therefore, by inserting (6) into (5), we have

As a result, the energy hub load matrix form is defined based on the input of energy carriers and energy storage devices as follows:where NL and NP are the number of energy hub loads and the number of energy carriers, respectively. C_i,j (t) is the power obtained from the energy carrier j for load i at hour t, which for an energy hub with three input carriers (electricity, gas, and heat) and two output loads (electricity and heat) is as follows:where C_ee (t), C_eg (t), and C_eh (t) are the electric power obtained from the transformer, CHP, and heat at hour t, respectively, and since there is no connection between the thermal carrier and the electric load, C_eh (t) is equal to zero. Similarly, the heat obtained from electrical carriers, CHP, and EH is represented by C_he (t), C_hg (t), and C_hh (t), respectively, where C_he (t) is equal to zero due to the lack of connection between the electrical carrier and the thermal load.

2.4. Formulation of Demand Response

Demand response means the ability of customers to improve their energy consumption patterns to reach appropriate prices and improve network reliability. In this regard, based on [44, 45], load shifting from peak load to low load is formulated as follows, if possible:where is the prespecific peak at peak load (Ω) and is the modified load model. A is the amount of load added to low load hours (Ψ) according to the incentive program.where α is the percentage of the energy reduced at peak load that is recovered at low load. In other words, the network operator encourages consumers to reduce consumption during peak periods. Instead, with incentive schemes, it encourages them to increase consumption during low loads to improve the consumption load pattern. At peak load, the network operator encourages users to reduce consumption with incentive plans. Assume the peak load at hour t is equal to P_T (t). As a result of using incentive plans, a percentage of peak network load is reduced and added to low load. So, we havewhere ξ is a coefficient of the total load at peak load. TP (t) is the amount of load that is reduced at hour t and is added to the time of low load.

2.5. Formulation of Distributed Generation

In order to bring the problem closer to reality, the capacity of the DG is randomly considered on the electric load side. Therefore, the random capacities to determine the total capacity are defined as follows:where C_s (t) is the matrix of random DG capacities at hour t. ρ is the initial value of the capacity, and μ_s (t) is a random value at hour t per sample s. Therefore, the final capacity of DG at hour t (FC (t)) is defined as follows:

Because the DG on the load side serves as a generator, the capacity of that electric load is reduced.where L_e^Total (t) is the matrix of final electric load at hour t. In the following, the results of the proposed method in different scenarios will be examined.

3. Case Study

In this section, the results of the proposed method are presented. The results are analyzed in four scenarios defined in Table 1. In addition, the prices of energy carriers (electricity, gas, and heat) are fixed in all scenarios and shown in Figure 2. The price of gas and heat is constant at all hours and is equal to 3 and 7 p.u., respectively. Also, the price of electricity varies by hour during the day [46]. In addition, transformers, CHP (gas to electricity and gas to heat), and HE are defined as hub energy converters in this paper, and the conversion factor of these converters is represented by C_ee, C_eg, C_hg, and C_hh, and their values are, respectively, equal to 0.985, 0.37, 0.43, and 0.9. Also, the charging and discharging losses of storage devices are omitted.

3.1. Energy Hub without DR and DG (Basic Scenario)

In the basic mode, there is no DRP or DG, and the generation of energy carriers and costs (generation cost and blackout, if any) are calculated. The electrical and thermal loads of EH in the basic mode at different hours of the day are shown in Figure 3. It can be seen that in some hours there is a peak load and that in other hours there is a low load. Therefore, it is expected that a part of the load will be shut down during peak hours.

In this regard, the amount of energy carrier generation is obtained based on the load of the EH, as shown in Figure 4. The gas energy carrier has played a role in supplying loads during peak hours (8 to 16, 18, and 19). In addition, a significant part of the electric load power is provided by the electric energy carrier because the transformer in the hub has a high conversion factor while the CHP conversion factor for converting the gas energy carrier into electricity is low and not cost-effective.

The electric vehicle charging station plays a role in the supply of electric loads as an electric storage hub of energy so that the amount of charging and discharging of the electric vehicle station is shown in Figure 5, which is proportional to the electric load in the basic mode; it is usually charged (receiving power) during low load hours and discharged (injecting power into the electric load) during peak network hours. The maximum capacity of this station is 1 p.u., which is specified in the figure in the hours when it reaches its maximum and minimum capacity. It is clearly known that during peak hours (15 to 21), the capacity of the EVCS has been reduced to a minimum over time, and in low load conditions, its capacity has reached its maximum.

Also, the rate of charging and discharging of the thermal storage to provide part of the thermal load is presented in Figure 6. The amount of power available in the HS at the beginning of work is equal to 1.5 p.u. It can be clearly seen that usually during the hours when the thermal load is maximum, the storage is discharged, and during the hours when the thermal load is minimum, it is charged. In other words, in hours 1, 10, 15, 16, 18, and 24, when the thermal load is minimal, the thermal storage is at the charging stage with values of 0.8, 0.9, 1.7, 1.9, 1.5, and 0.8 p.u., respectively. In addition, at hours 8, 9, 13, 20, to 22, when the thermal load is at its maximum, the thermal storage is discharging with values of −0.5, −1.2, −1.1, −1.2, −1.3, and −2 p.u., respectively.

Despite the generation of energy carriers and energy storage devices in basic mode, there is a blackout in hours 15, 16, 18, and 19. In this regard, the cost of unsupplied power during these hours is applied to other costs. As shown in Figure 7, the final cost in hours 15, 16, 18, and 19 is equal to 161.5, 160.7, 86.5, and 86.5 p.u., respectively. In the following, the impact of the DR program and DG on resolving the blackouts will be investigated. It can be clearly seen that the cost caused by the outage of the electrical load has a significant impact on the total cost during outage hours.

3.2. Energy Hub considering DR Program (Scenario 1)

As mentioned, in the basic mode, there is a blackout at 15, 16, 18, and 19 hours. Therefore, the DR program is used in this scenario to solve the blackout. To fix the blackout, 15% of the peak load (ξ = 0.15) is removed and added to the low load hours during the specified hours. Therefore, as shown in Figure 8, the final electric load has been reduced by applying the DRP during blackout hours, and the removed values have been added to low load hours (1, 3, 4, and 5). As a result, the total load is reduced during peak hours, leading to blackout relief.

In addition, the DRP also affects the charge and discharge rates of the HS. So, with the reduction of electric load during peak load hours, the amount of gas energy carrier generation changes, and these changes also affect the way thermal load is supplied. This phenomenon causes a change in the way the HS is charged and discharged, as illustrated in Figure 9.

Finally, the cost assigned to the energy carriers per generation of each carrier is shown in Figure 10. In this scenario, the outages caused by the DRP are fixed, and the total cost is significantly reduced compared to the basic scenario. In the following, the impact of DG on solving the blackout created in the basic scenario will be investigated.

3.3. Energy Hub considering DG (Scenario 2)

In this scenario, in order to bring the problem closer to reality, the uncertainty in the capacity of DG is considered. As shown in Figure 11, the average capacity of DG at hour 10 per 1000 samples is equal to 0.598 p.u., which reduces the electric charge in this hour.

Similarly, by averaging the samples obtained in other hours, the capacity of DG is calculated. Finally, according to Figure 12, the final electric load is obtained from the difference between the electric charge in the base mode and the capacity of DG.

Applying DG will eliminate blackouts created in the base mode. Therefore, the total production at different hours of the day and night for all factors (electricity, gas, heat, EVCS, and HS) is shown in Figure 13. The largest share of generation is related to the carrier of electric energy. Also, EVCS and HS devices have played a production role during peak hours.

In addition, the total cost of the production of energy carriers in this scenario is shown in Figure 14, where the electric energy carrier has the largest share due to its high price compared to other energy carriers. Also, the existence of DG has solved the outages in base mode. In this regard, the cost due to load shutdown in this scenario is zero.

3.4. Energy Hub considering DR and DG (Scenario 3)

This scenario considers both previous scenarios simultaneously. The cost due to this scenario is shown in Figure 15, where the highest cost is equal to 62 p.u. at hour 16, which is less than other scenarios. In addition, there is no outage cost due to electrical and thermal loads, but it is generally more economical than other scenarios.

3.5. Comparison of Scenarios

As shown in Figure 16, the basic scenario has a higher total cost than the other scenarios due to blackouts in some hours. The presence of DRP (scenario 1) eliminates the outages caused in the base case and reduces the total cost to 1062.9, but the effect of DG (scenario 2) is better. The amount of the total cost in scenario 2 is reduced to 926.9 due to the reduction of the final electrical load, which is better than other scenarios. Also, the cost assigned to the electric energy carrier is higher than the gas and heat carriers, and the blackout cost in scenarios 1 and 2 is zero. The power stored in the EVCS can be different in various scenarios. Finally, the total cost in scenario 3 (DR and DG) is economically better than the other scenarios, so the cost has reached 906.2 due to the simultaneous use of the DR program and DG.

As shown in Figure 17, the stored power in hours 2 to 4, 9, 10, and 17 to 20 in the presence of DG is less than in other scenarios because, in the presence of DG, the final electric load is reduced, and since the price of electricity is more expensive than other carriers, in order to optimize the cost, the capacity of the EVCS is used to meet the load. In addition, in some hours, due to the cheaper price of electricity compared to other hours, the capacity of the EVCS is greater than in other scenarios. In other words, the EVCS is charged.

Also, the capacity of HS in scenario 1 has decreased compared to the basic scenario in peak hours (16, 17, 18, and 19) in Figure 18. In addition, the power stored in the HS has increased with the presence of DG in hours 16, 17, and 18 compared to the base state. In the rest of the hours, the power stored in the thermal storage is the same in all scenarios.

4. Conclusion

This paper proposes a method based on linear programming that considers blackouts created by the load to optimize energy costs by considering energy converters and storage devices. In addition, it uses two tools, the DRP and DG, to solve the blackouts, and the capacity of the DG is associated with uncertainty in order to bring the issue closer to reality. The results are compared in several scenarios, and one of the most important results is the issue of costs. The cost of Scenario 2 (consideration DG) is more economical than Scenario 1 (consideration DR) and the basic scenario by 136 p.u. and 330.6 p.u., respectively. On the other hand, Scenario 3 is economically more economical than the other scenarios due to the simultaneous use of DRP and DG, so the total cost is equal to 906.2 p.u. In addition, it is possible to point out the correct behavior of ES devices, especially the EVCS, in different load conditions, which are charged at low load and discharged at peak load. From the comparison of the results, it can be clearly seen that the behavior of storage devices does not depend only on the decrease or increase of the load but also on the increase or decrease in the price of energy carriers. In addition, in the basic scenario, at hours 15, 16, 18, and 19, the load will be shut down by 0.57, 0.57, 0.07, and 0.07 p.u., respectively, which, with the help of DRP and DG, fixes the blackouts. The proposed method can be an effective tool for energy hub management, especially when there is a blackout, in order to fix it at the lowest cost.

Nomenclature

EH:	Energy hub
DR:	Demand response
DRP:	Demand response program
DG:	Distributed generation
UP:	Unsupplied power
ES:	Energy storage
NL:	Number of energy hub loads
NP:	Number of energy carriers
EVCS:	Electric vehicle charging station
HS:	Heat storage
α:	Percentage of the energy reduced peak load that is recovered in low load
ξ:	Coefficient of the total load at peak load
ρ:	The initial value of the capacity of DG
μ_s (t):	Random value at hour t per sample s for capacity of DG
Ω:	Peak load hours
Ψ:	Low load hours
λ_e (t):	Electricity price at hour t
(t):	Gas price at hour t
λ_h (t):	Heat price at hour t
p (t):	Basic load model at hour t
:	Modified load model at hour t
:	Prespecific peak
P_T (t):	Peak load at hour t
TP (t):	The amount of load that is reduced at hour t and is added to the time of low load
P_e (t):	Generation power of electricity energy carrier at hour t
(t):	Generation power of gas energy carrier at hour t
P_h (t):	Generation power of heat energy carrier at hour t
P_e^Min (t), P_e^Max (t):	Minimum/maximum generation power of electrical energy carrier at hour t
^Min (t), ^Max (t):	Minimum/maximum generation power of gas energy carrier at hour t
P_h^Min (t), P_h^Max (t):	Minimum/maximum generation power of thermal energy carrier at hour t
UP_e (t):	Unsupplied electric power at hour t
UP_h (t):	Unsupplied thermal power at hour t
Pen_e (t):	Penalty for unsupplied electric power at hour t
Pen_h (t):	Penalty for unsupplied thermal power at hour t
L (t):	Output load matrix at hour t
L_e^Total (t):	The matrix of the final electric load at hour t
C (t):	Coupling matrix at time t
C_s (t):	The matrix of randomly DG capacities at hour t
C_ee (t):	Electric energy carrier coefficient for electric load at hour t
C_eg (t):	Gas energy carrier coefficient for electric load at hour t
C_eh (t):	Heat energy carrier coefficient for electric load at hour t
Q^ch (t), Q^dis (t):	Charge/discharge matrix of energy storage devices at hour t
C_Q^ch (t), C_Q^dis (t):	Matrix of charging/discharging coefficients of energy storage devices at hour t.

Data Availability

The data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

P. Favre-Perrod, “A vision of future energy networks,” in Proceedings of the IEEE power engineering society inaugural conference and exposition in Africa, pp. 13–17, IEEE, Durban, South Africa, July 2005.
View at: Google Scholar
M. Geidl and G. Andersson, “Optimal power flow of multiple energy carriers,” IEEE Transactions on Power Systems, vol. 22, no. 1, pp. 145–155, 2007.
View at: Publisher Site | Google Scholar
M. Geidl, Integrated Modeling and Optimization of Multi-Carrier Energy Systems, ETH Zurich, 2007.
G. A. Koeppel, Reliability Considerations of Future Energy Systems: Multi-Carrier Systems and the Effect of Energy Storage, ETH, 2007.
G. Koeppel and G. Andersson, “Reliability modeling of multi-carrier energy systems,” Energy, vol. 34, no. 3, pp. 235–244, 2009.
View at: Publisher Site | Google Scholar
M.-H. Shariatkhah, M.-R. Haghifam, M. Parsa-Moghaddam, and P. Siano, “Modeling the reliability of multi-carrier energy systems considering dynamic behavior of thermal loads,” Energy and Buildings, vol. 103, pp. 375–383, 2015.
View at: Publisher Site | Google Scholar
M. C. Bozchalui, S. A. Hashmi, H. Hassen, C. A. Canizares, and K. Bhattacharya, “Optimal operation of residential energy hubs in smart grids,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 1755–1766, 2012.
View at: Publisher Site | Google Scholar
S. Paudyal, C. A. Cañizares, and K. Bhattacharya, “Optimal operation of industrial energy hubs in smart grids,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 684–694, 2015.
View at: Publisher Site | Google Scholar
A. D. Ondeck, T. F. Edgar, and M. Baldea, “Optimal operation of a residential district-level combined photovoltaic/natural gas power and cooling system,” Applied Energy, vol. 156, pp. 593–606, 2015.
View at: Publisher Site | Google Scholar
X. Zhang, M. Shahidehpour, A. Alabdulwahab, and A. Abusorrah, “Optimal expansion planning of energy hub with multiple energy infrastructures,” IEEE Transactions on Smart Grid, vol. 6, no. 5, pp. 2302–2311, 2015.
View at: Publisher Site | Google Scholar
M. Salimi, H. Ghasemi, M. Adelpour, and S. Vaez‐Zadeh, “Optimal planning of energy hubs in interconnected energy systems: a case study for natural gas and electricity,” IET Generation, vol. 9, no. 8, pp. 695–707, 2015.
View at: Google Scholar
M. Aghamohamadi and N. Amjady, “Modelling and optimisation for costly efficiency improvements on residential appliances considering consumer's income level,” IET Generation, Transmission & Distribution, vol. 11, no. 16, pp. 3992–4001, 2017.
View at: Publisher Site | Google Scholar
X. Zhang, L. Che, M. Shahidehpour, A. S. Alabdulwahab, and A. Abusorrah, “Reliability-based optimal planning of electricity and natural gas interconnections for multiple energy hubs,” IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1658–1667, 2017.
View at: Publisher Site | Google Scholar
M. S. Javadi, A. Esmaeel Nezhad, A. R. Jordehi, M. Gough, S. F. Santos, and J. P. Catalão, “Transactive energy framework in multi-carrier energy hubs: a fully decentralized model,” Energy, vol. 238, Article ID 121717, 2022.
View at: Publisher Site | Google Scholar
A. A. M. Aljabery, H. Mehrjerdi, S. Mahdavi, and R. Hemmati, “Multi carrier energy systems and energy hubs: comprehensive review, survey and recommendations,” International Journal of Hydrogen Energy, vol. 46, no. 46, pp. 23795–23814, 2021.
View at: Publisher Site | Google Scholar
M. La Scala, A. Vaccaro, and A. Zobaa, “A goal programming methodology for multiobjective optimization of distributed energy hubs operation,” Applied Thermal Engineering, vol. 71, no. 2, pp. 658–666, 2014.
View at: Publisher Site | Google Scholar
S. Bahrami, M. Toulabi, S. Ranjbar, M. Moeini-Aghtaie, and A. M. Ranjbar, “A decentralized energy management framework for energy hubs in dynamic pricing markets,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6780–6792, 2018.
View at: Publisher Site | Google Scholar
A. Shabanpour-Haghighi and A. R. Seifi, “Energy flow optimization in multicarrier systems,” IEEE Transactions on Industrial Informatics, vol. 11, no. 5, pp. 1067–1077, 2015.
View at: Publisher Site | Google Scholar
A. Najafi, H. Falaghi, J. Contreras, and M. Ramezani, “A stochastic bilevel model for the energy hub manager problem,” IEEE Transactions on Smart Grid, vol. 8, no. 5, pp. 2394–2404, 2017.
View at: Publisher Site | Google Scholar
Y.-G. Son, B.-C. Oh, M. A. Acquah, R. Fan, D.-M. Kim, and S.-Y. Kim, “Multi energy system with an associated energy hub: a review,” IEEE Access, vol. 9, 2021.
View at: Google Scholar
J. Wasilewski, “Integrated modeling of microgrid for steady-state analysis using modified concept of multi-carrier energy hub,” International Journal of Electrical Power & Energy Systems, vol. 73, pp. 891–898, 2015.
View at: Publisher Site | Google Scholar
T. Shekari, A. Gholami, and F. Aminifar, “Optimal energy management in multi-carrier microgrids: an MILP approach,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 4, pp. 876–886, 2019.
View at: Publisher Site | Google Scholar
D. Huo, S. Le Blond, C. Gu, W. Wei, and D. Yu, “Optimal operation of interconnected energy hubs by using decomposed hybrid particle swarm and interior-point approach,” International Journal of Electrical Power & Energy Systems, vol. 95, pp. 36–46, 2018.
View at: Publisher Site | Google Scholar
F. Kamyab and S. Bahrami, “Efficient operation of energy hubs in time-of-use and dynamic pricing electricity markets,” Energy, vol. 106, pp. 343–355, 2016.
View at: Publisher Site | Google Scholar
A. R. Jordehi, M. S. Javadi, M. Shafie-khah, and J. P. Catalão, “Information gap decision theory (IGDT)-based robust scheduling of combined cooling, heat and power energy hubs,” Energy, vol. 231, Article ID 120918, 2021.
View at: Publisher Site | Google Scholar
M. S. Javadi, R. Azami, and H. Monsef, “Security constrained unit commitment of interconnected power systems,” INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, vol. 4, no. 2, pp. 199–205, 2009.
View at: Google Scholar
M. S. Javadi, A. Anvari-Moghaddam, J. M. Guerrero, A. E. Nezhad, M. Lotfi, and J. P. Catalão, “Optimal operation of an energy hub in the presence of uncertainties,” in Proceedings of the IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe, pp. 1–4, IEEE, Genova, Italy, June 2019.
View at: Google Scholar
A. Iranpour Mobarakeh, R. Sadeghi, H. Saghafi Esfahani, and M. Delshad, “Optimal planning and operation of energy hub by considering demand response algorithms and uncertainties based on problem-solving approach in discrete and continuous space,” Electric Power Systems Research, vol. 214, Article ID 108859, 2023.
View at: Publisher Site | Google Scholar
J. Salehi, A. Namvar, F. S. Gazijahani, M. Shafie-khah, and J. P. Catalão, “Effect of power-to-gas technology in energy hub optimal operation and gas network congestion reduction,” Energy, vol. 240, Article ID 122835, 2022.
View at: Publisher Site | Google Scholar
M. Seyfi, M. Mehdinejad, B. Mohammadi-Ivatloo, and H. Shayanfar, “Scenario-based robust energy management of CCHP-based virtual energy hub for participating in multiple energy and reserve markets,” Sustainable Cities and Society, vol. 80, Article ID 103711, 2022.
View at: Publisher Site | Google Scholar
M. Aghamohamadi, M. Samadi, and M. Pirnahad, “Modeling and evaluating the energy hub effects on a price responsive load,” Iranian Journal of Electrical and Electronic Engineering, vol. 15, no. 1, p. 65, 2019.
View at: Google Scholar
D. Qiu, Z. Dong, X. Zhang, Y. Wang, and G. Strbac, “Safe reinforcement learning for real-time automatic control in a smart energy-hub,” Applied Energy, vol. 309, Article ID 118403, 2022.
View at: Publisher Site | Google Scholar
E. Mokaramian, H. Shayeghi, F. Sedaghati, A. Safari, and H. H. Alhelou, “An optimal energy hub management integrated EVs and RES based on three-stage model considering various uncertainties,” IEEE Access, vol. 10, pp. 17349–17365, 2022.
View at: Publisher Site | Google Scholar
J. Lei et al., “Energy management considering energy storage and demand response for smart energy hub in Internet of Things,” IEEE Access, 2020.
View at: Google Scholar
M. Aghamohamadi, M. Samadi, and I. Rahmati, “Energy generation cost in multi-energy systems; an application to a non-merchant energy hub in supplying price responsive loads,” Energy, vol. 161, pp. 878–891, 2018.
View at: Publisher Site | Google Scholar
A. Sheikhi, S. Bahrami, and A. M. Ranjbar, “An autonomous demand response program for electricity and natural gas networks in smart energy hubs,” Energy, vol. 89, pp. 490–499, 2015.
View at: Publisher Site | Google Scholar
J. Wang, H. Zhong, Z. Ma, Q. Xia, and C. Kang, “Review and prospect of integrated demand response in the multi-energy system,” Applied Energy, vol. 202, pp. 772–782, 2017.
View at: Publisher Site | Google Scholar
F. Brahman, M. Honarmand, and S. Jadid, “Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system,” Energy and Buildings, vol. 90, pp. 65–75, 2015.
View at: Publisher Site | Google Scholar
S. Bahrami and A. Sheikhi, “From demand response in smart grid toward integrated demand response in smart energy hub,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 1–658, 2015.
View at: Publisher Site | Google Scholar
A. Sheikhi, M. Rayati, S. Bahrami, and A. Mohammad Ranjbar, “Integrated demand side management game in smart energy hubs,” IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 675–683, 2015.
View at: Publisher Site | Google Scholar
M. Majidi, S. Nojavan, and K. Zare, “A cost-emission framework for hub energy system under demand response program,” Energy, vol. 134, pp. 157–166, 2017.
View at: Publisher Site | Google Scholar
M. Roustai, M. Rayati, A. Sheikhi, and A. Ranjbar, “A scenario-based optimization of Smart Energy Hub operation in a stochastic environment using conditional-value-at-risk,” Sustainable Cities and Society, vol. 39, pp. 309–316, 2018.
View at: Publisher Site | Google Scholar
N. Ploskas and N. Samaras, Linear Programming Using MATLAB®, Springer, Berlin, Germany, 2017.
M. Fotuhi-Firuzabad and R. Billinton, “Impact of load management on composite system reliability evaluation short-term operating benefits,” IEEE Transactions on Power Systems, vol. 15, no. 2, pp. 858–864, 2000.
View at: Publisher Site | Google Scholar
D. Huang, R. Billinton, and W. Wangdee, “Effects of demand side management on bulk system adequacy evaluation,” in Proceedings of the IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, pp. 593–598, IEEE, Singapore, June 2010.
View at: Google Scholar
M. Aghamohamadi, M. E. Hajiabadi, and M. Samadi, “A novel approach to multi energy system operation in response to DR programs; an application to incentive-based and time-based schemes,” Energy, vol. 156, pp. 534–547, 2018.
View at: Publisher Site | Google Scholar

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Copyright © 2023 Mohammad Hasan Nikkhah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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