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An Investigation on the Role of Energy Storage Usage in Residential Energy Hubs

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An Investigation on the Role of Energy Storage Usage in Residential Energy Hubs
M.H. Barmayoon
a
, M. Fotuhi-Firuzabad
b
, A. Rajabi-Ghahnavieh
a
, and M. Moeini-Aghtaie
b
a
Department of Energy Engineering,
b
Department of Electrical Engineering Sharif University of Technology Tehran, Iran Barmayoon@energy.sharif.edu, rajabi@sharif.edu, fotuhi@sharif.edu, and m.moeini@ieee.org
Abstract
—
Global warming issues, air pollution and dependence on fossil fuels are the major challenges threatening the sustainable usage of conventional energies for the next generations. Energy efficiency, which is the next big thing in energy policies of different countries, has triggered off new promising solutions like Energy hub. The energy hub presence in future vision of energy networks promotes new frameworks for operational and planning studies of different energy consumers. This paper tries to run an investigation on the role of various energy storage technologies in technical and financial attributes of residential energy hubs. In this regard, the mathematical model of a residential energy hub in presence of energy storage systems is extracted and then this model is involved in economic dispatch (ED) optimization problem. Eight different scenarios which represent different structures of a residential energy hub are introduced and the proposed comparative studies are applied to them.
Index Terms
—
Cooling and heating, economic dispatch, energy storage, home, residential energy hub.
I.
I
NTRODUCTION
Along with development of different energy conversion technologies,
humanity’s endeavors across every industrial
and commercial sector have been more dependent on various forms of energies such as electricity, natural gas, heating/cooling and etc. Therefore, a large number of equipment need to correctly cooperate with each other aimed to deliver different forms of energies to consumers. Conventionally, large-scale power plants were responsible for providing the required electrical energy. However, depletion of fossil fuel resources and global concern of climate changes prompted the role of more efficient distributed generation (DG) units such as renewable energy systems (RES) and combined heat and power (CHP) [1]. With the appearance of cogeneration technologies, new interactions between different energy infrastructures arises, while the operation of each energy infrastructure still have been planned individually. Recently, a new concept, i.e.
energy hub
, has been proposed to model the synergies among different energy carriers [2] and [3]. Indeed, energy hub is an interface between
different energy infrastructure in which energy carriers can be converted, stored and transferred though it. The energy hub concept opens a new window on study of synergies which can be provided by energy carriers and their network infrastructure. An energy hub feeds different energy loads through hybrid input and output ports and thus offers more flexibility in load supplying or peak shaving in prices. Therefore, some investigations concerning the new and future energy systems modeled based on the concept of energy hubs have been started [4], [5] and [1]. The efficacy of such a
combined or integrated analysis has already been recognized in a few recent works [6], [7], [2] and [8]. The authors in [6]
tried to model energy flow of the houses by the concept of energy hub. A method has been presented in [9] to find optimal size of the integrated CHP system and electrical energy storage system. In recent researches, energy hub concept is also used in demand response Program. Ref. [10] represent the modeling of a home as an energy up taking into account different appliance, CHP and PHEV. Then the residential demand is optimally managed in the presented framework. Although some articles such as [11] have been published on analysis of energy flows in residential loads by energy hubs, there are a few works in which the role of energy storage systems (both of electrical and thermal storage systems) have been highlighted in optimal operation of residential energy hubs. This issue is the main concern of this paper and we will try to run a comprehensive study to investigate the effects of various storage technologies on optimal operation of energy in residential loads. The system that will be discussed in this paper is a multi-input multi-output energy hub in which the electrical, heating and cooling demand at output ports are supplied through various energy carriers at the input ports including electricity and natural gas received via their networks. In this model, electricity can be fed directly from power grid. Also, in some cases there is a CHP system in the home which provides a portion of electricity. On the other hand, Boiler and CHP meet the heating demand. Third type of the residential energy demand is cooling that it is supplied by cooling systems such as air conditioner or absorption chiller. Furthermore,
Figure 1. General model of an Energy Hub Figure 2. Energy Hub model of a home
Photovoltaic (PV) system will be considered as some storage units in this model. Based on the structure of discussed residential energy hub, the role of electrical storage in reducing the operation cost of houses will be investigated. In this regard, the problem of economic dispatch in a residential energy hub is presented and its mathematical model is extracted. The attained optimization problem is modeled as a linear programming (LP) model. The energy hub concept and an overview of home energy hub modeling are presented in section 2. Section 3 explains the model assumptions and detail formulation of energy hub dispatch optimization problem. Various case studies are defined in section 4. In this section, the results of applying the proposed method are obtained and a sensitivity study is run and some key parameters have been analyzed. Finally, conclusions are drawn in section 5. II.
H
OUSING
E
NERGY
M
ODEL AS AN
E
NERGY
H
UB
In each house, there are many appliances which utilize different forms of energy to provide daily needs of residents. The way to optimally manage the energy flows in a house is
so called “housing energy model”. This model
determines that how any input energy carrier fed to the houses either via energy infrastructures or DGs in the houses will be delivered to the appliances. In the literature, many works can be found treating with this important issue and some different methods have been proposed to systematically model the energy flow variations in a house. In this paper, this model is based on energy hub concept.
A.
Energy Hub Concept
An integrated system in which the energy carriers can be converted, conditioned, and stored within it is referred to the energy hub. This well-known concept stands as an interface between energy carriers, their infrastructures and demand of energies. In an Energy hub, different forms of energies are received at its input ports connected to energy infrastructures, and deliver the transformed energy services such as electricity, heating, and cooling at its output ports [1]. Within the energy hubs, energy carriers are converted and conditioned using, e.g., transformers, power-electronic devices, CHP technology, heat exchangers, absorption chiller, and other equipment which are so called convertors. Fig. 1 illustrates different levels of a general energy hub model. At input level, an energy hub consumes different energy carriers received via infrastructures and inject these carriers to converters. Then energy carriers can be converted, conditioned and stored in the energy hub layer. Finally the different energy demands will be satisfied at the output ports. In an energy hub, the mapping of input energy carriers (L) to the loads at output ports (P) can
mathematically be modeled through defining a matrix named the coupling matrix (C). Each coupling factor (C
ij
) relates one certain input (i) to a particular output (j) and incorporates conversion efficiencies and dispatch factors that take into account the dispatch of an input energy carrier to the devices converting this energy carrier. Equation (1) provides the mathematical description of an energy hub:
L C C P L C C P
. (1)
B.
Energy Modeling in Residential Energy Hubs
To model the energy flows in a house through the concept of energy hub, at first, these questions need to be answered:
Which energy carriers are fed to the house either via urban energy infrastructures?
What converters are applied in the house to transform the energy from one form to the other one?
How can the energy requirements of different appliances in the house can be categorized as various types of energy loads? Once these questions are answered, the housing energy model can be followed by the concept of energy hub. Figure 2 as an illustrative example depicts the energy flows model in a residential area based on the concept of energy hub. As shown in this figure, the energy demands of a house are categorized into three different groups including electricity utilized for home lighting and the other electrical equipment, cooling needs, heating and hot water demand. The received power from distribution network, natural gas along with the produced power of a photovoltaic are the input carrier of this energy hub. The electricity requirements of this house can be supplied via three different paths. The electric received via the network,
the output power of photovoltaic panel and the electrical output of the CHP unit can be utilized to supply this form of energy or to be stored in storage. The cooling requirements of this house is provided either by the air conditioner or absorption chiller. The output of boiler and CHP unit can be employed to either supply the heat demand of this house or to be stored in heat storage for future usage. Fig. 2 is a fairly comprehensive model and different operation modes of these equipment will be discussed in Section IV. As can be followed in the recent discussion, the energy hub represents a certain degree of flexibility in supplying different forms of energies. This flexibility is due to the hub redundancy, i.e., there are different paths to provide a certain output energy carrier. This redundancy not only leads to more flexibility in optimizing the energy requirements of a house, but also, can offer higher reliability levels to the customers. However, proposing a systematic framework to optimize the operating strategy of such an energy hub seems inevitable. III.
E
CONOMIC
D
ISPATCH
P
ROBLEM IN A
R
ESIDENTIAL
E
NERGY
H
UB
In a house and from viewpoint of its owner, the main goal is to minimize energy consumption and subsequently minimizing the cost of energy. To achieve this goal, the operator can control the amount of different energy carriers and the dispatch factor of them in input of converters. Besides, the amount of energy stored in different storage technologies are the other control factors that can determine the imposed cost of supplying energy in a house.
A.
Model Assumptions
During optimization of the model, some important assumptions are followed:
Customer decisions are taken based only on the reduction in its energy bill.
The CHP equipment can operate anywhere between 0% and 100% of its rated capacity, and ramping rate for load adjustment is not included.
The CHP unit is assumed to be 100% reliable. Based on these assumptions, the economic dispatch optimization problem can be formulated as follows:
B.
Formulation of Energy Dispatch optimization in a House
The objective of the model is to minimize the operating cost of supplying electricity, cooling and heating demands. The total cost (TC) can be calculated as follows:
1
TC
t
N t t t t t t e e g g GTe gt
EP OC P P PF E P v
. (2) The total cost is the sum of the received electricity from network and fuel (gas) cost and the operation cost of CHP system. In this equation, EP
e
and EP
g
is electricity and gas price respectively that is considered for electricity (P
e
) and gas (P
g
) energy consumption. The operation cost factor (OCF) of CHP is considered 0.006 $ per kilowatt-hour production in
CHP’s output.
The power flow coupling between the input and output of energy hub as a constraint of this optimization is described by
0 11 0
t t t t e c pv t t t t C T GTee ie oet t t t gGTh F t t h c AC
L L Pv P Q QPv v L L
(3) This energy hub supply electricity (
t e
L
), heating (
t h
L
) and cooling (
t c
L
) load in output port. The demanded energy of home is supplied through photovoltaic system (P
pv
) and different converters. Efficiency of transformer, boiler, cooler and chiller are defined as
T
,
F
,
C
,
AC
respectively. Also CHP system has two efficiency parameter for electricity (
GTe
) and heating (
GTh
) outputs. In input port, gas fuel is divided between CHP system and boiler in proportion of
t
v
factor. Furthermore the cooling load of the home can be met through cooler and chiller in proportion of
t
factor. Other variables that should be calculated, are the rate of charging
t ie
Q
and rate of discharging
t oe
Q
. The relation presented in (4) is modeled as an equality constraint in this optimization problem. The variations in state of charge (SOC) of storage unit within the period of t is:
1
1/
t stb t t t e e e ie ESi oe ESo
E E E Q Q
. (4) In stated equation,
ESi
and
ESo
are efficiency of input and output of electrical storage. Based on rate of charging, level of stored energy (
t e
E
) will be changed. Moreover,
stbe
E
specify the percent of standby energy losses in storage system. Inequality constraints are given by power and energy limits of the converter and storage elements. Each converter has a certain capacity that cannot supply more than.
t e T
P P
. (5)
t t GTe g CHP
v P P
. (6)
1
t t F g F
v P P
. (7)
t t c C
L P
. (8)
1
t t c AC
L P
. (9) Electricity storage is limited in imported and exported energy. Furthermore, it is obvious that storage units store limited energy within themselves, i.e.
t ie ie ie
Q Q Q
. (10)
t oe oe oe
Q Q Q
. (11)
Figure 4. Different energy carriers price Figure 5. Renewable systems output TABLE
I.
V
ARIOUS
C
ASE
S
TUDIES
Case Energy Hub Elements
Trans. Boiler CHP Cooler Chiller PV
1
2
3
4
5
6
7
8
Figure 3. Energy demand profile
t e e e
E E E
. (12) In order to obtain sustainable storage utilization, another equality constraint can be included in the problem formulation which requires that the storage energies at the end of the last period of the studied time interval are equal to the initial energies:
124
e e
E E
. (13) At last, Due to conservation of power, the dispatch factors must fulfill the following requirements:
0 1
t
v
. (14)
01
t
. (15) In general, this formulation represents a nonlinear constrained optimization problem. The solvability of the problem depends on the actual equations used. Once the objective function is convex and all constraints are expressed as linear equations, the solution space is convex and the global optimum can be determined using numerical methods. Otherwise, numerical methods have to be used in order to find a solution within the feasible region, but global optimality cannot be guaranteed. IV.
C
ASE STUDIES
A.
System Under study and Various Cases
In this section, aimed to investigate the role of energy storage units on operating conditions of energy carriers in a typical house, eight different cases are introduced and implemented on the energy hub presented in Fig. 2. These eight cases are itemized in Table I. Running the economic dispatch optimization problem on these cases, the results are obtained and analyzed. To run this analysis, the typical load profiles of a house (electricity and heat load profiles) in a residential region in Tehran is utilized. Energy load profiles of a sample house are depicted in Fig. 3. These diagrams show the yearly average energy demand of the under studied home. Moreover, the tariffs of energy carriers received via the networks, i.e. electricity and natural gas are considered similar to the ones used in Iran. The variations of these tariffs in a typical day is presented in Fig. 4. As can be traced in this figure, the tariff of natural gas in Iran is constant during different hours of a day. In contrast, the electricity tariff gets various values at different hours. There are three levels for this tariff; off-peak (2.3 cent), mid-peak (3.3 cent) and on-peak (4 cent). The other parameter which should be determined before running the economic dispatch studies of the energy hub is the variations in output generation of PV unit. Solar technologies are well known due to their abilities in reducing the emissions generated in energy sector. At each time, the output generation of solar panels is a function of the solar radiation. Due to variability of solar irradiation, power output of PV units should be modeled as a nondeterministic and stochastic variable. The total power output of a PV unit in a specified future time period cannot be determined due to the variations
Figure 6. Electricity supply Figure 7. Heating supply TABLE
II.
A
C
OMPARISON ON
O
PERATING
C
OSTS OF THE
E
NERGY
H
UB
U
NDER
S
TUDY WITH
/
WITHOUT STORAGE SYSTEMS
Case Total Operating Cost
Without Storage System With Electricity Storage
1 360.16 354.8 2 301.9 296.5 3 278.7 273.3 4 220.5 215.1 5 252.6 241.3 6 209.5 196.3 7 179.9 166.9 8 147.6 136.4
of weather conditions, control strategies, and shadow conditions, etc. The forecast of power output, particularly the short-term forecast (say one-day ahead), is a challenging task for PV power system as the power output varies largely with the external conditions like sunshine, temperature, etc. However, according to the recorded data of solar radiation in Tehran, average production of solar photovoltaic in a sample home (4 cubic meter of system) is illustrated in Fig. 5.
B.
Results and Discussion
Based on the assumptions addressed in previous part, the economic dispatch optimization problem is run in GAMS environment and the optimum energy flow in energy hub elements is attained. Table II compares the operating costs of the under studied energy hub with and without storage systems. As can be seen in this table, applying storage unit in case 6 has caused a noticeable reduction in operating cost of the energy hub compared to the other cases. This observation can be justified as follows. In this case, i.e. case 6, the electrical demand can be supplied by the received power from the network or the gas burned in CHP unit to produce power or the output power of PV unit. At times that the cost of supplying power from the network is more than the CHP unit, the output power of this unit will be utilized. Moreover, at times that the PV unit can supply all or a portion of the electricity demand, it gets a higher priority in supplying the load of system. Therefore, in this case, the hub operator should pay less compared to case 1 - case 5. Once the storage unit is also added to the other elements of the energy hub, the operating cost of the energy hub would decrease more. This is due this fact that in this situation at the times that either produced power of CHP or the PV unit is more than the required demand, the operator can charge that battery (duration between 6 a.m. and 1 p.m.). This stored energy can be utilized in the peak period (duration between 6 p.m. and 9 p.m.) and as a consequence reduces the cost of energy hub (13.2 $ reduction in total cost). The other benefit of using storage returns to its ability in reducing the peak power required from the network from 1.3 to 0.9 kW. It is clearly can be seen from Fig. 6. As was shown in Fig. 7, heating output of CHP system supply the substantial part of heating demand of house and boiler should supply the extra part of that. The other observation which should be discussed in the results shown in table II is the presence of PV unit. As can be traced in these results, in the cases that PV unit is considered, i.e. case 2, case 4, case 6 and case 8, the operating cost of system is remarkably reduced compared to the other ones. This is due to low operating cost of these renewable energies. In addition, the presence of chiller in case 4 and case 8 leads to lower imposed costs in comparison with the cases which supply their cooling demand by cooler (case 2 and case 6).
C.
Sensitivity Analysis
Up to now, it has been discussed that how the presence of different converters and storage units can affect the operating cost of energy hub. In what follows, it will be shown that how the changes in some important parameters like the electricity tariff and size of the CHP unit can determine the profitability of storage units. Figures 8 and 9 respectively represent the sensitivity analysis on the effects of electricity tariff and CHP unit size. Zero point of the horizontal axis in these diagrams shows the base condition of the studies. Based on the results shown in Fig. 8, it can be traced that as long as the electricity tariff increases more than the base case, the amount of reduction in operating cost of the energy hub for all the cases is remarkable. These conditions for cases having CHP units (case 5
–
case 8) is more sensible. The presence of CHP unit can give the hub operator the flexibility

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