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Modelling and optimization of CHP based district heating system with renewable energy production and energy storage
Applied Energy 159 (2015) 401–421
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Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modelling and optimization of CHP based district heating system with renewable energy production and energy storage Haichao Wang a,c,⇑, Wusong Yin b, Elnaz Abdollahi a, Risto Lahdelma a, Wenling Jiao c a
Department of Energy Technology, Aalto University School of Engineering, P.O. Box 14100, FI-00076, Aalto, Finland SiChuan KeHong Oil & Gas Engineering Co. Ltd, Sichuan Province 610051, China c School of Municipal & Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China b
h i g h l i g h t s An efficient modelling and optimization method is proposed for CHP based DH system. Renewable energy source and energy storage is promoted in the CHP-DH system. The optimal planning and operation of the CHP-DH system can be solved. Thermal storage will be used more often and extensively when CHP load fluctuates. Future scenarios with a higher share of RES and a bigger TES are simulated.
a r t i c l e
i n f o
Article history: Received 5 May 2015 Received in revised form 2 September 2015 Accepted 3 September 2015
Keywords: Optimization Combined heat and power (CHP) District heating (DH) Renewable energy source (RES) Energy storage Energy efficiency
a b s t r a c t Renewable energy source (RES) is playing an increasingly important role to reduce fossil fuels in district heating (DH) and to alleviate the accompanying environmental impact. In this paper, a combined heat and power (CHP) based DH system with RES and energy storage system (ESS) is studied. A modelling and optimization method is developed for planning and operating such CHP-DH systems. The objective of the optimization is to minimize the overall costs of the net acquisition for heat and power in deregulated power market. A planning model consisting of energy balances and constraints for system control and operation is built and an efficient algorithm is developed. We demonstrate the method in a CHP-DH system with a solar thermal plant and a thermal energy storage (TES). Results indicate that the developed method is efficient and flexible for planning and operating CHP-DH systems. To simulate the future situation, we also optimize the same CHP-DH system with a higher share of RES and a bigger TES. Results show that TES is used more intensively in the future with more fluctuating CHP load and a higher share of RES. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Renewable energy sources (RES) are playing an increasingly important role in the energy sector around the world [1,2]. Many countries and international organizations have made scenarios and set targets to facilitate a RES-based future energy system [3]. The share of RES in gross final energy consumption in EU28 countries reached 14.1% in 2012 and it should be increased to 20% by 2020 [3]. Finland as a leading country in RES has decided to increase their RES share to 38% by 2020 [3]. The World Wide Fund
⇑ Corresponding author at: Department of Energy Technology, Aalto University School of Engineering, P.O. Box 14100, FI-00076, Aalto, Finland. Tel.: +358 (0)46 539 1597; fax: +358 (09) 470 23674. E-mail address: [email protected] (H. Wang). http://dx.doi.org/10.1016/j.apenergy.2015.09.020 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
for nature (WWF) described a provocative scenario of a world with 100% renewable energy by 2050 [2]. According to the report, it is not just the best choice to switch to RES, but it is the only option in the future, because the way the world produces and uses energy today is not sustainable. However, this doesn’t mean that the transition is not cost-effective; on the contrary, integrating RES gradually in the energy system can be affordable and sustainable by the global economy and the resource-scarce planet in the future. Meanwhile, this helps solve most of the problems related to climate change and dwindling fossil fuels. The International Energy Agency (IEA) reported that out of all energy consumption, heating and cooling accounted for about 46% of the total global energy use in 2012 [4] and almost half of end use of energy in Europe is heat. District heating (DH) accounts for a major part of this energy consumption. For example, in China
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Nomenclature Abbreviations CHP combined heat and power CCHP combined cooling, heating and power DH district heating EES electric energy storage ESS energy storage system HOB heat-only boiler LP linear programming MILP mixed integer linear programming RES renewable energy source TES thermal energy storage Symbols C c cp (cj, pj, qj)
cost of combined heat and power production, € the specific heat capacity of water, J/(kg °C) electricity market price, €/MW h characteristic point of CHP in terms of cost, power and heat J index set of characteristic points of CHP P power generation, MW h p power load, MW pu,ramp power ramping, MW/h max pmax maximum charging and discharging rates of EES, chr and pdis MW Q heat production, MW h q heat load, MW qt;max and qt;max maximum charging and discharging rates of TES chr dis in time step t, MW
over 36% of the total building energy demand is consumed for residential heating purposes [5] and about 62.9% of district heat is produced by combined heat and power (CHP) [6]. In Finland, although the specific energy consumption for DH is gradually reduced, the share of space heating out of the total end use of energy increased from 21% to 25% during 2005–2012 [7,8]. In fact, DH accounts for about 50% of the total heating market in Finland and almost 80% of the district heat is produced efficiently in CHP plants. This means that CHP is playing an indispensable role in producing the district heat and far from out-of-date. Especially the biomass CHP plants connected to DH networks are recognized as a very good potential to increase the share of RES in energy systems [9]. However, this is the first step in reaching the targets of RES contribution and for mitigating climate change; more RES should be integrated in the CHP-DH systems to promote the use of renewable energy and to further increase the energy efficiency. Moreover, the intermittent and non-controllable nature of RES should be considered by fulfilling users’ demand with other controllable sources. Thermal energy storage (TES) is often used for exploiting the RES [10,11]. In EU, there are already many practices and pilot projects integrating RES such as solar, wind and geothermal into the DH systems. A micro network in Crailsheim, Germany uses solar thermal energy and TES for DH. Negative residual power from e. g. wind farm has been used for DH in Germany [12] and Greece [13] through the power-to-heat infrastructures. The smart thermal grid at the TU Delft campus provides sustainable central heating and cooling energy through CHP, geothermal, thermal storage and surface water. It is reported that the system has achieved 10% energy savings, representing a reduction of 0.4 Mton equivalent CO2 per year [14]. In Nordic countries, integration of RES in DH is also in progress. Different technologies e.g. CHP, heat pumps, solar panels, waste-to-energy and even free cooling combined with
Sq
heat storage level, MW h
St;min and St;max minimum and maximum heat storage levels in q q time step t, MW h Sp power storage level, MW h Spmin and Spmax minimum and maximum electricity storage levels, MW h T planning horizon, d ts and tr supply and return water temperatures in the DH network, °C U set of all plants in CHP-DH system V water volume in the TES, m3 Z net acquisition cost, € x the decision variable encoding the convex combination of the CHP operating region eu,ramp power ramping rate, % gqs storage efficiency of TES, % gps storage efficiency of EES, % q water density, m3/kg Superscripts and subscripts chr charging dis discharging p power pq power and heat, CHP q heat t time step
renewable fuels are more often needed because of the high share of RES. Although CHP is deemed as a conventional energy source and DH is a mature technology, we still need to make full use of them because they are still playing an indispensable role in the energy sector and far from out-of-date. Therefore, a CHP-DH system with RES and energy storage is a promising solution before reaching the 100% renewable society, and it can be seen as part of the 4th generation district heating (4GDH) proposed by Lund et al. [15]. The CHP-DH system is not a totally new concept, but it is difficult to determine ideal system configurations such as dimensioning CHP, peak heat sources and share of RES. It is also difficult to control and operate such a multi-faceted energy systems optimally. In all, system-level modelling and optimization of the configuration and operation of CHP-DH systems is more important to yield energy efficiency and minimal CO2 emissions in real-life cases. Carpaneto et al. [10] developed an optimization procedure to find the dispatching strategy of CHP, boilers, solar collectors and TES. The optimization procedure can be used at the planning level to find out the best sizing proportions of solar and conventional sources and for defining the optimal capacity for the storage. Buoro et al. [16] identified the optimal energy production system and its optimal operation strategy for a distributed energy supply system consisting of a CHP plant, a DH network, a solar thermal plant and conventional components such as boilers and compression chillers. The optimization algorithm is based on a Mixed Integer Linear Programming (MILP) model that minimizes the total annual cost. Giuntoli and Poli [17] developed a branch and bound MILP algorithm to optimize the day-ahead thermal and electrical scheduling of a large scale virtual power plant which contains many smallscale producers, consumers, energy storages and cogeneration processes. Nuytten et al. [18] reported that the stochastic nature of the energy systems on the supply side requires increased flexibility at
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the demand side. They developed a model that determined the theoretical maximum of flexibility of a CHP system coupled to centralized or decentralized TES. Similarly, Ali et al. [19] optimized the demand response control of electric space heating with a partial thermal storage using a Linear Programming (LP) model in order to minimize the total energy cost for customers without sacrificing user comfort. Chesi et al. [20] used a TRNSYS unsteady model to optimize the thermal storage size in the context of combined cooling, heating and power (CCHP) with RES. Rong et al. [21] developed a Lagrangian relaxation based algorithm to optimize the trigeneration system with storages. Dagdougui et al. [22] stated that hybrid energy systems with RES can help improve the economic and environmental sustainability to fulfill the energy demand and they developed a dynamic model to integrate different RES and one storage device to feed a ‘‘Green” building for its thermal and electrical energy demands in a sustainable way. The abovementioned studies are either focused on daily or weekly planning or optimization to analyze the specific system in short periods. They are very helpful for operating the corresponding system in short term if the input data is sufficient. However, short term models are not reliable to determine the optimal system configuration, because more operating conditions during a much longer period should be considered for that purpose. The novelty of this study is justified by bridging this gap through developing an optimization model for the CHP-DH system in a monthly horizon and demonstrating a more efficient solution of the model. The objectives of the developed model are twofold; it can be used to determine the system configuration and it can also be applied for optimizing the planning and operating strategy of the CHPDH system in the long run, since it can provide flexibly very detailed data of decision variables. Further, this study contributes to better understanding the CHP-DH systems with RES and energy storage, and also other integrated energy systems consisting of CHP units. In the monthly horizon, more details can be modelled concerning different CHP-DH system components and their relationships, which facilitate more concrete planning and operation of the system. The model problem can be very large due to a large number of decision variables and constraints. Therefore we apply an efficient algorithm – LP2 [23,24] based on the sparse revised simplex to solve the problem. LP2 can run in LP or MILP mode depending on the problem formulation. In this paper, LP mode is used for solving the sample CHP-DH system in order to help understand how to optimally plan the operation of the CHP-DH system.
403
Fig. 1. Characteristic points and feasible operating region of a CHP (c = cost, p = power, q = heat) [25].
C¼
X c j xj ;
P¼
X pj xj ;
j2J
j2J
X qj xj ; Q¼ P
ð1Þ
j2J
j2J xj
¼ 1;
xj P 0; j 2 J: Here C is the production cost of the CHP. The cost can be a combination of fuel costs, service costs, environmental cost, taxes, etc.; P and Q are the power and heat production; J is the index set of characteristic points of the plant; xj is the decision variable encoding the convex combination of the operating region. The hourly multiple plants CHP planning model including the power and heat balance, convexity and non-negativity constraints can be established based on the single plant model. A multi-period planning model can further be formed as a sequence of hourly models connected by dynamic constraints. Note that the planning horizon can vary from a few days to one or even several years in a strategic long-term model [26]. The long-term CHP planning model can be solved by decomposing it into hourly models which are solved independently using linear programming (LP) [23], mixed integer linear programming (MILP) [27] or other fast algorithms [28] according to the detailed problem formulation. 2.2. Modelling and optimization of CHP-DH system with renewables and storages
2. System modelling and optimization methodologies 2.1. Characteristic points of CHP and single CHP planning model The production of heat and power in CHP is highly coupled. Moreover, heat and/or power are produced at different efficiencies and costs in different operating regions of the CHP plant. A CHP plant has the highest possible efficiency when operated at full power under the designed optimal power to heat ratio. This operating point is one of the so-called characteristic points of the feasible operating region. For example, Fig. 1 shows a typical operating region with six characteristic points in terms of cost, power and heat (c, p, q). The characteristic operating region is assumed to be convex in terms of heat and power, and the cost is also a convex function of the corresponding heat and power production [25]. The convexity of an operating region means that if the CHP can operate at two different points, it can also operate at any point on the line segment connecting them. Based on this assumption, the hourly cost, power and heat production of a CHP plant can be represented as convex combinations of characteristic points (cj, pj, qj):
2.2.1. A CHP-DH system with renewable energy production and energy storages A CHP-DH system should be able to satisfy heat and power demand with minimal cost and environmental impact in a sustainable and reliable manner. Such a system may also contain separate power and heat production plants e.g. heat-only boilers (HOB) and condensing power plants to make the energy supply more flexible and reliable. In addition, the share of renewable energy for DH shall be gradually increased. However, renewable energy production is not in line with the demand because the availability is uncontrollable and intermittent. Therefore, we propose a CHP-DH system in combination with RES, e.g. solar thermal plant and energy storage system (ESS) to promote the utilization of RES in DH. Fig. 2 shows the schematic energy flow in different time step and between different components of a CHP-DH system consisting of CHP, HOB, condensing power plant and solar thermal plant. Nevertheless, other RES, e.g. wind power can also be taken into account according to local conditions. TES and electric energy storage (EES) can be used to satisfy the heat and power demand. The system can
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Fig. 2. Schematic energy flow in a CHP-DH system with renewable energy production and energy storages.
also incorporate other energy supplies and demands, e.g. micro CHP plants, distributed generators of heat and power, and even electric vehicles depending on the local conditions. In this paper, we assume that heat is locally distributed according to the local heat demand and power is uploaded to the market. This is justified, since thermal energy is easier to store while electricity is more suitable for long-distance transmission. 2.2.2. The objective of the CHP-DH planning model For the abovementioned CHP-DH system, the energy production is primarily planned based on the heat demand, because the lack of heat is not permitted, and surplus heat must usually be disposed of at a cost, even though it can be partly stored in the TES. The objective of CHP-DH planning is to minimize the overall net acquisition cost for energy. The net acquisition cost includes production costs (fuel and other costs) subtracted by revenue from selling the generated electricity on the deregulated power market. The objective function can be written as
min Z ¼
" T X t¼1
X
! ctj xtj
#
ctp Pt
;
ð2Þ
j2Ju ;u2U
where Z is the net acquisition cost; T is the length of the planning horizon in hours; Ju is the set of extreme points of plants u; U is the set of all plants in the system; ctp and Pt are the electricity market price and the generated electricity in time step t. 2.2.3. Balances for heat, power and storages The heat balance of the CHP-DH system means that the district heat demand is satisfied by CHP and other heat production facilities including the TES. In this model, the charging and discharging efficiencies of the storages are taken into account. Eq. (3) is the heat balance for the CHP-DH system. Similarly, the power balance is written as in Eq. (4).
X q
j2J u ;u2U [U
X p
j2J u ;u2U [U
ðqj xj Þt qtchr þ gqdis qtdis ¼ Q t ;
ð3Þ
ðpj xj Þt ptchr þ gpdis ptdis ¼ Pt ;
ð4Þ
pq
pq
Here U is the set of all plants; Up, Uq, Upq are the sets of condensing power plants, heat plants and CHP plants respectively; qtchr, qtdis, ptchr, ptdis are the charging and discharging amounts of heat and power in time step t; gqdis, gpdis are the discharging efficiencies of heat and power; Qt is the heat demand in time step t. The solar heat production is modelled using EnergyPRO [29]. In the modelling, we assume that the initial storage level equals the storage level at the end of the planning horizon. Specifically, the thermal energy storage level at current time step t is affected by the storage level at previous time step (t 1) and the current charging and discharging heat from the storage. Therefore the storage efficiency [30] should also be taken into account besides charging or discharging efficiency [31,32]. The reason is that a small part of the previously stored heat will be lost due to the heat conductivity and heat radiation [31]. Therefore, the thermal energy storage balance can be expressed in Eq. (5), similarly the electricity storage balance can be written as in Eq. (6).
Stq ¼ gqs St1 þ gqchr qtchr qtdis ; q
ð5Þ
þ gpchr ptchr ptdis ; Stp ¼ gps St1 p
ð6Þ
Stq
Stp
where and are the storage level for heat and power at the end of time step t; gqs and gps are the storage efficiencies of heat and power; gqchr, gpchr are the charging efficiencies for heat and power. 2.2.4. Constraints for system control and operation The constraints for energy storage systems include: (1) the storage level constraints in Eqs. (7) and (8) which mean that the
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storage level should be in a range between the minimum and maximum for the safety and economy reasons; (2) the constraints of charging and discharging in Eqs. (9)–(12) indicating that the charging and discharging rates should be smaller than the corresponding maximum allowed rates for the economic and reliable operation of the CHP-DH system.
405
where q is the water density; V is the water volume of the TES; c is the specific heat capacity of water, c = 4.2 kJ/(kg °C); ts and tr are the supply and return water temperatures in the DH network. The convexity and non-negativity constraints for CHP are expressed in Eqs. (14) and (15). Non-negativity constraint for other plants is a little different and shown in Eq. (16).
X
xj ¼ 1;
ð14Þ
St;min 6 Stq 6 St;max ; q q
ð7Þ
Smin 6 Stp 6 Smax ; p p
ð8Þ
xj P 0; j 2 J u ; u 2 U pq ;
ð15Þ
0 6 qtchr 6 qt;max chr ;
ð9Þ
0 6 xj 6 1; j 2 J u ; u R U pq :
ð16Þ
t;max 0 6 qtdis 6 qdis ;
ð10Þ
t;max ; 0 6 ptchr 6 pchr
ð11Þ
0 6 ptdis 6 pt;max dis ;
ð12Þ
where Sqt;min , Sqt;max are the minimum and maximum heat storage max are the minimum and maximum eleclevels in time step t; Smin p , Sp t;max t;max are the maximum charging and districity storage levels; qchr , qdis max charging rates of TES in time step t; pmax chr , pdis are the maximum
charging and discharging rates of EES. The storage capacity of TES is influenced by the supply and return water temperatures in DH network. The storage capacity of TES equipped with unpressurized water tanks is calculated by Eq. (13), which considers water temperatures in the top and the bottom of the TES. A temperature difference of 5 degrees from the DH supply and return temperatures is considered in the calculation because of the temperature drop in the DH network. Moreover, a maximum temperature in the top level of is set 98 °C for an unpressurized TES.
Smax ¼ q
1 qVcfmin½ðts 5Þ; 98 ðtr þ 5Þg; 3600
ð13Þ
j2J u ;u2U pq
The power ramping constraint, which restricts how much the power production of a CHP plant may increase or decrease between two successive time steps is important in real-life operation of a CHP plant. In many cases, power ramping rate can be expressed as MW/h or sometimes can be the percentage of the power capacity of the plant. In this model, the power ramping constraint is,
jðpj xj Þtu ðpj xj Þt1 j 6 pu;ramp ; u
ð17Þ
or
jðpj xj Þtu ðpj xj Þt1 j=ðpj xj Þut1 6 eu;ramp ; u
ð18Þ
where pu,ramp is power ramping rate in MW/h, and eu,ramp is power ramping percentage. In addition, the maintenance constraint for CHP can be also considered in this model, because in some periods the CHP plant should be shut down for maintenance. In Eq. (19), the maintenance period is from time step Tm to Tm+n during the planning horizon.
xj ¼ 0; j 2 Ju ; t 2 ½T m ; T mþn :
ð19Þ
2.2.5. Solving the planning model The CHP-DH planning model (2)–(19) can be solved as an LP problem. In this model, the heat and power balances are taken into
Fig. 3. Schematic energy flow in a CHP-DH system with a solar thermal plant and a TES.
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account, and the energy balances as well as constraints for energy storages are considered. In addition, the power ramping constraint and maintenance constraint are also introduced to make the model more accurate. For a long time horizon, the model may become very large. In that case, it can be solved using special decomposition techniques, by solving hourly models separately and coordinated under top-level iteration to consider dynamic constraints [21]. In this study we have solved the model using LP2, which is based on the sparse revised Simplex and the product form of inverse [23,24]. LP2 can run in LP or MILP mode depending on the problem formulation. In this study, we use the LP mode in the following case study in order to reveal more details behind the algorithm and help understand the process of making an optimal planning and operation of such CHP-DH systems.
Table 2 Capacities and efficiencies of the three CHP plants. CHP plants
CHP 1
CHP 2
CHP 3
Capacity
12.5 MWe 38 MWth 97%a 87%a
11 MWe 36 MWth 85% 80%
8.4 MWe 17 MWth 85% 80%
High efficiency Low efficiency a
With flue gas heat recovery.
HOBs are also indicated and the efficiencies of the two HOBs are 90%. Generated electricity is transmitted to the market but heat is distributed to the local heat users and/or to the TES. Besides, the stored heat can be extracted when the heat demand is high but the heat production is insufficient. The charging and discharging rates of the TES are assumed half the storage capacity at each time step, which means that the TES can store or discharge at maximum half the capacity during one time step. In addition, the economic data related to the objective function in Eq. (2) are shown in Table 1. Other input data are described in the next section.
3. Case study: optimizing a CHP-DH system with a solar thermal plant and a TES 3.1. System description A CHP-DH system with a solar thermal plant equipped with solar panels and a heat storage using unpressurized water tanks is planned in a city community of south Finland. The schematic of the system is shown in Fig. 3 and it consists of,
3.2. Model input The most important data input for the CHP-DH planning model include the characteristic points of the three CHPs, the heat demand profile of the region, the market electricity price and solar radiation density. The characteristic points of the three CHPs are demonstrated in Fig. 4. Firstly, CHP plants cannot operate in the lower left corners of each plane. There is a minimum load for each CHP plant, if the actual load is smaller than the minimum load, then the CHP should be shut down. Secondly, for CHP 2 and CHP 3, only two characteristic points are available and they correspond
3 CHP plants, CHP 1 and CHP 2 are fed by biomass (wood chips) and CHP 3 by natural gas; 2 HOBs, HOB 1 fed by natural gas and HOB 2 by heavy oil; 1 solar thermal plant with 10,000 m2 of solar panels; 1 TES with 10,000 m3 water tanks. Fig. 3 shows the nominal fuel inputs, net electrical and heat outputs of the three CHP plants. Fuel inputs and heat loads of
Table 1 Economic data of the CHP-DH system planning model. Fuel prices (€/MW h)
a b c
Fuel taxes (€/MW h)
Efficiencies of TES
Power ramping
Wood
Natural gas
Oil
Wood
Natural gas
Oil
Charging
Storage
Discharging
21
40
100a
0
13.664b
10.199b
0.95
0.998c
0.95
10%
Estimated oil price based on the market prices; all prices are deemed stable during the planning period. Starting at year 2015. Estimated at U value 0.2 W/(m2 K), average temperatures in and out of the tanks 60 °C and 3 °C.
(a) CHP 1 – biomass
(b) CHP 2 – biomass
(c) CHP 3 – natural gas
Fig. 4. Characteristic points of the three CHP plants (f = fuel, p = power, q = heat). (a) CHP 1 – biomass, (b) CHP 2 – biomass, (c) CHP 3 – natural gas.
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Fig. 5. Heat demand profile of the region in January and July, 2011.
Fig. 6. Hourly spot electricity price of the region in January and July, 2011.
Fig. 7. Hourly solar radiation density of the region in January and July, 2011.
Table 3 System components of the CHP-DH system scenarios. Scenario
1 2 3 4
CHP
HOB
CHP 1
CHP 2
CHP 3
HOB 1
HOB2
d d d
d d d
d d d
d d d d
d d d d
Solar thermal plant
TES
DH load
Electricity market
d d d d
d d d d
d d d d
d d d d
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
CHP 2
CHP 3
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(a) scenario 1 CHP 2
CHP 3
HOB 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(b) scenario 2 CHP 1
CHP 3
HOB 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
672
720
Time (h)
(c) scenario 3 CHP 1
CHP 2
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
Time (h)
(d) scenario 4 Fig. 8. Heat production of the CHP-DH system vs. heat demand in January.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
30
CHP 2
CHP 3
Power (MW)
25 20 15 10 5 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
528
576
624
672
720
528
576
624
672
720
528
576
624
672
720
Time (h)
(a) scenario 1 CHP 2
30
CHP 3
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(b) scenario 2 CHP 1
30
CHP 3
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(c) scenario 3 CHP 1
30
CHP 2
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(d) scenario 4 Fig. 9. Power generation of the CHP-DH system in January.
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to the two extreme working points. However, we obtained a third characteristic point for CHP 1 based on analyzing the real-life operating records. Thirdly, the variation of efficiency due to the load changing of CHP is taken into account in the characteristic points and operating region. Rankine cycle is used in two biomass CHP plants, therefore their characteristic curves are almost the same; however biomass CHP 1 has a little higher efficiency because it is a new plant and the waste heat in flue gas is also recovered for DH. In CHP 3, an open-cycle gas turbine with heat recovery boiler is used. Table 2 shows the capacities and efficiencies of the three CHP plants. The heat demand profile of this region during 2011 is obtained and shown in Fig. 5. The profile includes the heat demand for space heating and for heating the tap water. In addition, the outdoor temperature, supply and return temperatures of the DH network are also obtained and used to calculate the capacity of the TES. The spot electricity prices and the solar radiation density of this region are shown in Figs. 6 and 7. We can find that the electricity prices in winter is a little higher than in summer, and the hourly variations of the prices are very clear. As can be seen from Fig. 7, the solar radiation density is rather low in winter time in Finland, but very high in summer time. 3.3. Results The data of January and July were used to demonstrate the developed model and to optimize the planning and operation of the CHP-DH system. According to the system components shown in Fig. 3, there are three CHP plants and two HOBs in this CHPDH system, but it is found that the district heat demand is smaller than the heat outputs altogether. This means that all the three CHP plants are not necessarily needed at the same time for DH. Therefore, the CHP-DH scenarios studied in this paper are shown in Table 3, where the cross means the corresponding CHP is not included. We can optimize the system using the MILP mode of LP2, but LP mode is used in this study in order to reveal more details behind the algorithm and help understand the process to make the optimal planning and operation of the CHP-DH system. In the following, the results of January and July are demonstrated consecutively. Fig. 8 shows the heat production vs. heat demand for each CHPDH system scenario in January, 2011. We find that all CHP-DH systems produce heat well according to the heat demand, but the HOBs are not needed in scenarios 1 and 4. In scenario 1 shown in
Fig. 8(a), CHP 3 (fed by natural gas) is kept running at minimum load due to the high fuel cost, and CHP 1 and 2 often operate at partial loads in order not to produce surplus heat. In scenario 4 shown in Fig. 8(d), two biomass CHP plants (1 and 2) will coordinate with each other to satisfy the heat demand, and they need to operate in partial loads frequently but the average loads are a little higher than that in scenario 1. As can be seen from Fig. 8(b) and (c), if the gas-fired CHP 3 is planned with either CHP 2 or CHP 1, then the biomass CHP will operate at full load in the whole month, and CHP 3 operate in full load when heat demand is high; however when they are still insufficient, HOB 1 will be activated for flexible peak heating, which can follow the heat demand accurately. In addition, the heat output from the solar thermal plant is rather small, because of the extremely low solar radiation density in Finland in January. It can also be found that HOB 2 (fed by oil) is not necessarily needed even in the severe cold period under the current system configuration and heat demand profile. Fig. 9 shows the power generation of each CHP-DH scenario in January, 2011. It can be found that the power generation is highly coupled with heat production of CHP plants. The total fuel consumption and gross electricity generation of all CHP-DH scenarios are shown in Fig. 10, in which we find that the maximum difference of gross electricity generation is only 6% between scenarios 2 and 3. CHP plants in Scenario 2 generates less electricity and thus less heat, therefore HOB 1 will produce more heat, which is justified by the fuel consumption of HOB 1 in Fig. 10. Even all three CHP plants are running in scenario 1, the gross electricity generation is less than scenario 3 consisting of only CHP 1 and 3. In addition, scenario 4 with two biomass CHP plants can have a more efficient combined heat and power production than that in scenario 1 due to a higher average CHP load. These also indicates that the three CHP plants are not planning efficiently in scenario 1. The fuel consumption of CHP plants and HOBs are shown in Fig. 11. It can be seen from Fig. 11(a) that CHP 3 is running with minimum load and other CHP plants often operates with fluctuating partial loads. One way to improve this is to get more characteristic points, but it is better to remove one CHP plant from the analysis according to the current heat demand profile and to make the rest CHP plants operate more efficiently. This improvement can be observed in Fig. 11(b) and (c), where the biomass CHP (1 or 2) is always running in the design condition as the base plant, and the other CHP operates with high average load. In the last scenario, two biomass CHP plants are also running under high average loads but fuel consumption is much less without the need of gas.
Fig. 10. Total fuel consumption and gross electricity generation of the CHP-DH scenarios in January.
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Fuel consumption (MW)
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CHP 3
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Fuel consumption (MW)
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HOB 1
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Fuel consumption (MW)
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HOB 1
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(d) scenario 4 Fig. 11. Fuel consumption of the CHP plants and the HOB in January.
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Discharging heat load
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(a) scenario 1 Charging heat load
Discharging heat load
25 20 15 10 5 0 -5 -10 -15 -20 -25 48
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(b) scenario 2 Charging heat load
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Discharging heat load
20 15 10 5 0 -5 -10 -15 -20 -25 48
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(d) scenario 4 Fig. 12. Charging and discharging heat load of the thermal energy storage in January.
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Heat storage (MWh)
450 Heat storage capacity
400 350 300 250 200 150
Heat storage level
100 50 0 48
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Time (h)
(a) scenario 1 500
Heat storage (MWh)
450 400
Heat storage capacity
350 300 250 200 150 Heat storage level
100 50 0 48
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Time (h)
(b) scenario 2 500
Heat storage (MWh)
450 Heat storage capacity
400 350 300 250 200
Heat storage level
150 100 50 0 48
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Time (h)
(c) scenario 3 500
Heat storage (MWh)
450 400
Heat storage capacity
350 300 250 200
Heat storage level
150 100 50 0 48
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Time (h)
(d) scenario 4 Fig. 13. Heat storage capacity and storage levels in January.
The charging and discharging heat loads of the TES are shown in Fig. 12. We conclude from Fig. 12(a) and (d) that the TES is used more frequently if CHP 1 and 2 are considered in the
modelling (scenarios 1 and 4), this is because CHP 1 and 2 often operate in partial loads which fluctuate very sharply and no one can be the base plant in most of the planning horizon. Therefore
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Fig. 14. Objective function of the CHP-DH scenarios in January.
the TES is often used to balance the heat production and the heat demand. However, in other two scenarios, one of the biomass CHP plants (1 or 2) is running as the base plant, HOB1 is utilized as the peak boiler [33], which produces district heat to balance the heat production and the heat demand, therefore the use of TES is less. The heat storage levels are illustrated in Fig. 13. In this study, we use 99.8% hourly storage efficiency to optimize the CHP-DH system. The result indicates that TES is used more often and the storage level is generally growing higher when the loads of CHP plants are fluctuating, because more storage will be needed to balance the heat production and heat demand. The objective function in Eq. (2) which is defined as the total cost of net acquisition is shown in Fig. 14. We can see that the values of objective function are so different. Scenario 4 with two biomass CHP plants has the smallest cost and scenario 2 is most expensive, which is about 170% larger than that of scenario 4. This is because of the high fuel cost of natural gas consumed in CHP 3 and HOB 1. In addition, the costs of scenarios 1 and 3 are about 43% and 119% larger than that of scenario 4 respectively. Therefore, the CHP-DH scenario 4 with two biomass CHP plants is more preferred under the current system configuration and heat demand profile. For CHP-DH scenario 4, the optimization results in July, 2011 are shown in Fig. 15. It is clear that the heat load is much lower, and thus one of the biomass CHP plants (CHP 2) is shut down. During this period the service and maintenance can be implemented. The solar thermal plant produces more renewable heat thanks to the high solar radiation density in July. The power generation is also coupled with the heat production, and the power ramping constraint (10%) is also satisfied as shown in Fig. 15(b). Note that the heat storage capacity is much lower than that in January and the use of TES is much less than that in January, because CHP 1 operates smoothly near the minimum load. Finally, Fig. 16 shows the thermal storage percentage of TES at each time step during January and July. Thermal storage percentage is defined as the ratio between heat storage level and the heat storage capacity at each time step. We found that both the heat storage level and storage capacity are fluctuating hourly, therefore storage percentage is defined to indicate the use of the TES. It can be found that thermal storage percentage in January is higher than
in July, but sometimes it is not small in summer. There are two reasons for this: (1) although the CHP is running at minimum load, there is surplus heat due to the low heat demand and high solar thermal output in July; (2) the storage capacity is smaller in July due to the low DH temperature as shown in Fig. 15(e), and thus more TES volume will be needed in July to store a certain amount of heat than in January. 3.4. Discussion – future situation with a higher share of RES and a larger TES The above analyses are based on a real case having a small solar heat plant and a TES. However, more solar panels and heat storage tanks can be built in the future to meet the climate change target. Therefore, in order to simulate the future situation of the CHP-DH system with a higher share of solar heating and a larger TES, scenarios 5 and 6 shown in Table 4 are studied. In the two future scenarios, the areas of solar panels are either two or four times bigger than that in the current real case and the TES volume is also doubled. Meanwhile, the DH load is assumed to increase 20%; DH load increases slowly because the buildings are becoming more energy efficient in the future. 3.4.1. Heat production Heat production vs. heat demand in January and July for the two future scenarios are shown in Fig. 17. It can be seen that even the capacity of solar thermal plant is much bigger, solar heating still contributes only 0.1–0.2% to the DH in January, but in July the share of solar heat can reach 15.4–30.7%. In addition, the bigger TES enable the biomass CHP plants to operate as smoothly as possible with high average loads compared with Fig. 8(d). 3.4.2. The usage of TES The usage of TES is demonstrated in Figs. 18 and 19. As can be seen from Fig. 18, the TES will be used more extensively in January than in July, and the charging and discharging loads are larger than the current case of scenario 4, shown in Fig. 12(d). In addition, Fig. 19(a) and (b) show that the TES is fully used sometimes by storing the surplus heat from CHP so that CHP plants can operate with high average loads to increase the production efficiency. According to Fig. 19(c) and (d), the TES capacity is smaller in July.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0
48
96
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432
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672
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Time (h)
(a) scenario 4 - heat production v.s. heat demand in July CHP 1
30
Power (MW)
Power (MW)
25 20 15
6 5 4 3 2 1 0 260
265
270
275
280
285
290
336
384
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Time (h)
10 5 0
48
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Time (h)
Fuel consumption (MW)
(b) scenario 4 - power generation CHP 1
60 55 50 45 40 35 30 25 20 15 10 5 0 48
96
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336
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480
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Time (h)
Charging or discharging heat load (MW)
(c) scenario 4 - fuel consumption Charging heat load
25
Discharging heat load
20 15 10 5 0 -5 -10 -15 -20 -25 48
96
144
192
240
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336
384
432
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Time (h)
(d) scenario 4 - charging and discharging heat load Fig. 15. CHP-DH system optimization results in July.
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Heat storage (MWh)
450 400 350 300
Heat storage capacity
250 200 150 100 Heat storage level
50 0 48
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Time (h)
(e) scenario 4 - heat storage capacity and storage levels Fig. 15 (continued)
Fig. 16. Storage percentage of the TES in January and July.
Table 4 System components of the future CHP-DH scenarios. Scenario
5 6
CHP CHP 1
CHP 2
d d
d d
HOB1
Solar thermal plant
TES
DH load
Electricity market
d d
d (20,000 m2) d (40,000 m2)
d (20,000 m3) d (20,000 m3)
d (20% increase) d (20% increase)
d d
We observe that if the solar panels are increased to 40,000 m2, then CHP 1 will operate more smoothly near the minimum load and thus the TES will be used less often, but charging and discharging loads are a little bigger, finally resulting in smaller thermal storage percentage. 3.4.3. Fuel consumption, power generation and objective function The total fuel consumption and gross electricity generation of the future scenarios are shown in Table 5. We conclude that the bigger solar thermal plant can reduce the fuel consumption of the CHP-DH system, but not too much because the CHP plants still have to generate a certain amount of electricity and make profit so that the total cost of acquisition in Eq. (2) can be minimized.
However, more fuel can be saved if the HOB supplies a bigger proportion of the heat demand in a CHP-DH system. In Fig. 20, the objective function – total cost of acquisition are compared between the current case and the future scenarios. Note that in the two future scenarios, DH load is increased by 20%. It can be found that in January, the objective functions of scenarios 5 and 6 are greater than that of the current case, because of the increased heat demand and low contribution from solar. The objective function of scenario 6 is a little smaller than that of scenario 5 thanks to the slightly higher share of solar heat. The objective function of the two future scenarios in July are almost the same with current case, that is to say, the increased 20% of DH load can be satisfied by the solar heating.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
CHP 2
HOB 1
Solar (20000m2)
Heat demand increased by 20%
120 110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
96
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720
Time (h)
(a) scenario 5 - January CHP 1
CHP 2
HOB 1
Solar (40000m2)
Heat demand increased by 20%
120 110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
96
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(b) scenario 6 – January CHP 1
120
Solar (20000m2)
Heat demand increased by 20%
110 100
Heat load (MW)
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(c) scenario 5 - July CHP 1
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Solar (40000m2)
Heat demand increased by 20%
110 100
Heat load (MW)
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(d) scenario 6 - July Fig. 17. Heat production vs. heat demand in January and July for future scenarios 5 and 6.
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Charging or discharging heat load (MW)
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Discharging heat load - January
Charging heat load (TES=20000m3, Solar panel=20000m2) - January
40 30 20 10 0 -10 -20 -30 -40
48
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Time (h)
Charging or discharging heat load (MW)
(a) scenario 5 - January 40
Charging heat load (TES=20000m3, Solar panel=40000m2) - January
Discharging heat load - January
30 20 10 0 -10 -20 -30 -40 48
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(b) scenario 6 – January 40
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Discharging heat load - July
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Time (h)
Charging or discharging heat load (MW)
(c) scenario 5 - July 40
Charging heat load (TES=20000m3, Solar panel=40000m2)- July
Discharging heat load - July
30 20 10 0 -10 -20 -30 -40 48
96
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Time (h)
(d) scenario 6 - July Fig. 18. Charging and discharging heat load of the thermal energy storage in January for the future scenarios.
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heat storage level - January
1000
Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
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(a) scenario 5 - January Heat storage capacity (TES=20000m3, Solar panel=40000m2) - January
heat storage level - January
1000
Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(b) scenario 6 – January Heat storage capacity (TES=20000m3, Solar panel=20000m2) - July
heat storage level - July
1000
Heat storage (MWh)
900 800 700 600 Heat storage capacity
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heat storage level - July
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Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
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(d) scenario 6 - July Fig. 19. Heat storage capacity and storage levels in January for the future scenarios.
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Table 5 Total fuel consumption and gross electricity generation of the future CHP-DH scenarios (MW h). Scenario – month
Scenario Scenario Scenario Scenario
5 6 5 6
– – – –
January January July July
Fuel consumption
Gross electricity generation
CHP 1
CHP 2
HOB 1
37,325 37,285 11,466 11,209
39,163 39,108 – –
1683 1670 – –
16,816 16,809 2326 2247
Fig. 20. Comparison of the objective function for current case and future scenarios.
4. Conclusions
References
Renewable energy sources (RES) will be used more extensively in the future combined heat and power based district heating (CHP-DH) systems. In this paper, a CHP-DH system with RES and energy storage system (ESS) is proposed to promote the use of RES and to approach the climate change mitigation target in the context of secured energy supply and scarce resources. A modelling and optimization method was developed for the CHP-DH system in order to operate the system optimally. The model takes into account the energy balances, power and heat. To make the model more accurate and realistic, constraints on energy storage and the power ramping are also included. This large CHP-DH planning problem was solved using an efficient LP solver. The modelling and optimization method was demonstrated with a CHPDH system with a solar thermal plant and a thermal energy storage (TES). The same system was also optimized with a higher share of RES and a larger TES in order to simulate the future situation. The results indicate that the developed modelling and optimization method is efficient and flexible for planning and operating the CHP-DH systems. It can also be used for optimizing the combination of system components and the sizing problems in the future. It was found that the storage efficiencies should be well considered when optimizing the operation of the CHP-DH system. The optimal operation of the TES is influenced by both the heat demand and power price. The TES is used more intensively in the future with a more fluctuating CHP load and a higher share of RES.
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Acknowledgments This study is funded by project STEEM (Sustainable Transition of European Energy Market) Aalto University, Finland and by the Twelfth Five-Year-Plan of China under grant number 2012BAJ04B01.
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Contents lists available at ScienceDirect
Applied Energy journal homepage: www.elsevier.com/locate/apenergy
Modelling and optimization of CHP based district heating system with renewable energy production and energy storage Haichao Wang a,c,⇑, Wusong Yin b, Elnaz Abdollahi a, Risto Lahdelma a, Wenling Jiao c a
Department of Energy Technology, Aalto University School of Engineering, P.O. Box 14100, FI-00076, Aalto, Finland SiChuan KeHong Oil & Gas Engineering Co. Ltd, Sichuan Province 610051, China c School of Municipal & Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China b
h i g h l i g h t s An efficient modelling and optimization method is proposed for CHP based DH system. Renewable energy source and energy storage is promoted in the CHP-DH system. The optimal planning and operation of the CHP-DH system can be solved. Thermal storage will be used more often and extensively when CHP load fluctuates. Future scenarios with a higher share of RES and a bigger TES are simulated.
a r t i c l e
i n f o
Article history: Received 5 May 2015 Received in revised form 2 September 2015 Accepted 3 September 2015
Keywords: Optimization Combined heat and power (CHP) District heating (DH) Renewable energy source (RES) Energy storage Energy efficiency
a b s t r a c t Renewable energy source (RES) is playing an increasingly important role to reduce fossil fuels in district heating (DH) and to alleviate the accompanying environmental impact. In this paper, a combined heat and power (CHP) based DH system with RES and energy storage system (ESS) is studied. A modelling and optimization method is developed for planning and operating such CHP-DH systems. The objective of the optimization is to minimize the overall costs of the net acquisition for heat and power in deregulated power market. A planning model consisting of energy balances and constraints for system control and operation is built and an efficient algorithm is developed. We demonstrate the method in a CHP-DH system with a solar thermal plant and a thermal energy storage (TES). Results indicate that the developed method is efficient and flexible for planning and operating CHP-DH systems. To simulate the future situation, we also optimize the same CHP-DH system with a higher share of RES and a bigger TES. Results show that TES is used more intensively in the future with more fluctuating CHP load and a higher share of RES. Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction Renewable energy sources (RES) are playing an increasingly important role in the energy sector around the world [1,2]. Many countries and international organizations have made scenarios and set targets to facilitate a RES-based future energy system [3]. The share of RES in gross final energy consumption in EU28 countries reached 14.1% in 2012 and it should be increased to 20% by 2020 [3]. Finland as a leading country in RES has decided to increase their RES share to 38% by 2020 [3]. The World Wide Fund
⇑ Corresponding author at: Department of Energy Technology, Aalto University School of Engineering, P.O. Box 14100, FI-00076, Aalto, Finland. Tel.: +358 (0)46 539 1597; fax: +358 (09) 470 23674. E-mail address: [email protected] (H. Wang). http://dx.doi.org/10.1016/j.apenergy.2015.09.020 0306-2619/Ó 2015 Elsevier Ltd. All rights reserved.
for nature (WWF) described a provocative scenario of a world with 100% renewable energy by 2050 [2]. According to the report, it is not just the best choice to switch to RES, but it is the only option in the future, because the way the world produces and uses energy today is not sustainable. However, this doesn’t mean that the transition is not cost-effective; on the contrary, integrating RES gradually in the energy system can be affordable and sustainable by the global economy and the resource-scarce planet in the future. Meanwhile, this helps solve most of the problems related to climate change and dwindling fossil fuels. The International Energy Agency (IEA) reported that out of all energy consumption, heating and cooling accounted for about 46% of the total global energy use in 2012 [4] and almost half of end use of energy in Europe is heat. District heating (DH) accounts for a major part of this energy consumption. For example, in China
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Nomenclature Abbreviations CHP combined heat and power CCHP combined cooling, heating and power DH district heating EES electric energy storage ESS energy storage system HOB heat-only boiler LP linear programming MILP mixed integer linear programming RES renewable energy source TES thermal energy storage Symbols C c cp (cj, pj, qj)
cost of combined heat and power production, € the specific heat capacity of water, J/(kg °C) electricity market price, €/MW h characteristic point of CHP in terms of cost, power and heat J index set of characteristic points of CHP P power generation, MW h p power load, MW pu,ramp power ramping, MW/h max pmax maximum charging and discharging rates of EES, chr and pdis MW Q heat production, MW h q heat load, MW qt;max and qt;max maximum charging and discharging rates of TES chr dis in time step t, MW
over 36% of the total building energy demand is consumed for residential heating purposes [5] and about 62.9% of district heat is produced by combined heat and power (CHP) [6]. In Finland, although the specific energy consumption for DH is gradually reduced, the share of space heating out of the total end use of energy increased from 21% to 25% during 2005–2012 [7,8]. In fact, DH accounts for about 50% of the total heating market in Finland and almost 80% of the district heat is produced efficiently in CHP plants. This means that CHP is playing an indispensable role in producing the district heat and far from out-of-date. Especially the biomass CHP plants connected to DH networks are recognized as a very good potential to increase the share of RES in energy systems [9]. However, this is the first step in reaching the targets of RES contribution and for mitigating climate change; more RES should be integrated in the CHP-DH systems to promote the use of renewable energy and to further increase the energy efficiency. Moreover, the intermittent and non-controllable nature of RES should be considered by fulfilling users’ demand with other controllable sources. Thermal energy storage (TES) is often used for exploiting the RES [10,11]. In EU, there are already many practices and pilot projects integrating RES such as solar, wind and geothermal into the DH systems. A micro network in Crailsheim, Germany uses solar thermal energy and TES for DH. Negative residual power from e. g. wind farm has been used for DH in Germany [12] and Greece [13] through the power-to-heat infrastructures. The smart thermal grid at the TU Delft campus provides sustainable central heating and cooling energy through CHP, geothermal, thermal storage and surface water. It is reported that the system has achieved 10% energy savings, representing a reduction of 0.4 Mton equivalent CO2 per year [14]. In Nordic countries, integration of RES in DH is also in progress. Different technologies e.g. CHP, heat pumps, solar panels, waste-to-energy and even free cooling combined with
Sq
heat storage level, MW h
St;min and St;max minimum and maximum heat storage levels in q q time step t, MW h Sp power storage level, MW h Spmin and Spmax minimum and maximum electricity storage levels, MW h T planning horizon, d ts and tr supply and return water temperatures in the DH network, °C U set of all plants in CHP-DH system V water volume in the TES, m3 Z net acquisition cost, € x the decision variable encoding the convex combination of the CHP operating region eu,ramp power ramping rate, % gqs storage efficiency of TES, % gps storage efficiency of EES, % q water density, m3/kg Superscripts and subscripts chr charging dis discharging p power pq power and heat, CHP q heat t time step
renewable fuels are more often needed because of the high share of RES. Although CHP is deemed as a conventional energy source and DH is a mature technology, we still need to make full use of them because they are still playing an indispensable role in the energy sector and far from out-of-date. Therefore, a CHP-DH system with RES and energy storage is a promising solution before reaching the 100% renewable society, and it can be seen as part of the 4th generation district heating (4GDH) proposed by Lund et al. [15]. The CHP-DH system is not a totally new concept, but it is difficult to determine ideal system configurations such as dimensioning CHP, peak heat sources and share of RES. It is also difficult to control and operate such a multi-faceted energy systems optimally. In all, system-level modelling and optimization of the configuration and operation of CHP-DH systems is more important to yield energy efficiency and minimal CO2 emissions in real-life cases. Carpaneto et al. [10] developed an optimization procedure to find the dispatching strategy of CHP, boilers, solar collectors and TES. The optimization procedure can be used at the planning level to find out the best sizing proportions of solar and conventional sources and for defining the optimal capacity for the storage. Buoro et al. [16] identified the optimal energy production system and its optimal operation strategy for a distributed energy supply system consisting of a CHP plant, a DH network, a solar thermal plant and conventional components such as boilers and compression chillers. The optimization algorithm is based on a Mixed Integer Linear Programming (MILP) model that minimizes the total annual cost. Giuntoli and Poli [17] developed a branch and bound MILP algorithm to optimize the day-ahead thermal and electrical scheduling of a large scale virtual power plant which contains many smallscale producers, consumers, energy storages and cogeneration processes. Nuytten et al. [18] reported that the stochastic nature of the energy systems on the supply side requires increased flexibility at
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the demand side. They developed a model that determined the theoretical maximum of flexibility of a CHP system coupled to centralized or decentralized TES. Similarly, Ali et al. [19] optimized the demand response control of electric space heating with a partial thermal storage using a Linear Programming (LP) model in order to minimize the total energy cost for customers without sacrificing user comfort. Chesi et al. [20] used a TRNSYS unsteady model to optimize the thermal storage size in the context of combined cooling, heating and power (CCHP) with RES. Rong et al. [21] developed a Lagrangian relaxation based algorithm to optimize the trigeneration system with storages. Dagdougui et al. [22] stated that hybrid energy systems with RES can help improve the economic and environmental sustainability to fulfill the energy demand and they developed a dynamic model to integrate different RES and one storage device to feed a ‘‘Green” building for its thermal and electrical energy demands in a sustainable way. The abovementioned studies are either focused on daily or weekly planning or optimization to analyze the specific system in short periods. They are very helpful for operating the corresponding system in short term if the input data is sufficient. However, short term models are not reliable to determine the optimal system configuration, because more operating conditions during a much longer period should be considered for that purpose. The novelty of this study is justified by bridging this gap through developing an optimization model for the CHP-DH system in a monthly horizon and demonstrating a more efficient solution of the model. The objectives of the developed model are twofold; it can be used to determine the system configuration and it can also be applied for optimizing the planning and operating strategy of the CHPDH system in the long run, since it can provide flexibly very detailed data of decision variables. Further, this study contributes to better understanding the CHP-DH systems with RES and energy storage, and also other integrated energy systems consisting of CHP units. In the monthly horizon, more details can be modelled concerning different CHP-DH system components and their relationships, which facilitate more concrete planning and operation of the system. The model problem can be very large due to a large number of decision variables and constraints. Therefore we apply an efficient algorithm – LP2 [23,24] based on the sparse revised simplex to solve the problem. LP2 can run in LP or MILP mode depending on the problem formulation. In this paper, LP mode is used for solving the sample CHP-DH system in order to help understand how to optimally plan the operation of the CHP-DH system.
403
Fig. 1. Characteristic points and feasible operating region of a CHP (c = cost, p = power, q = heat) [25].
C¼
X c j xj ;
P¼
X pj xj ;
j2J
j2J
X qj xj ; Q¼ P
ð1Þ
j2J
j2J xj
¼ 1;
xj P 0; j 2 J: Here C is the production cost of the CHP. The cost can be a combination of fuel costs, service costs, environmental cost, taxes, etc.; P and Q are the power and heat production; J is the index set of characteristic points of the plant; xj is the decision variable encoding the convex combination of the operating region. The hourly multiple plants CHP planning model including the power and heat balance, convexity and non-negativity constraints can be established based on the single plant model. A multi-period planning model can further be formed as a sequence of hourly models connected by dynamic constraints. Note that the planning horizon can vary from a few days to one or even several years in a strategic long-term model [26]. The long-term CHP planning model can be solved by decomposing it into hourly models which are solved independently using linear programming (LP) [23], mixed integer linear programming (MILP) [27] or other fast algorithms [28] according to the detailed problem formulation. 2.2. Modelling and optimization of CHP-DH system with renewables and storages
2. System modelling and optimization methodologies 2.1. Characteristic points of CHP and single CHP planning model The production of heat and power in CHP is highly coupled. Moreover, heat and/or power are produced at different efficiencies and costs in different operating regions of the CHP plant. A CHP plant has the highest possible efficiency when operated at full power under the designed optimal power to heat ratio. This operating point is one of the so-called characteristic points of the feasible operating region. For example, Fig. 1 shows a typical operating region with six characteristic points in terms of cost, power and heat (c, p, q). The characteristic operating region is assumed to be convex in terms of heat and power, and the cost is also a convex function of the corresponding heat and power production [25]. The convexity of an operating region means that if the CHP can operate at two different points, it can also operate at any point on the line segment connecting them. Based on this assumption, the hourly cost, power and heat production of a CHP plant can be represented as convex combinations of characteristic points (cj, pj, qj):
2.2.1. A CHP-DH system with renewable energy production and energy storages A CHP-DH system should be able to satisfy heat and power demand with minimal cost and environmental impact in a sustainable and reliable manner. Such a system may also contain separate power and heat production plants e.g. heat-only boilers (HOB) and condensing power plants to make the energy supply more flexible and reliable. In addition, the share of renewable energy for DH shall be gradually increased. However, renewable energy production is not in line with the demand because the availability is uncontrollable and intermittent. Therefore, we propose a CHP-DH system in combination with RES, e.g. solar thermal plant and energy storage system (ESS) to promote the utilization of RES in DH. Fig. 2 shows the schematic energy flow in different time step and between different components of a CHP-DH system consisting of CHP, HOB, condensing power plant and solar thermal plant. Nevertheless, other RES, e.g. wind power can also be taken into account according to local conditions. TES and electric energy storage (EES) can be used to satisfy the heat and power demand. The system can
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Fig. 2. Schematic energy flow in a CHP-DH system with renewable energy production and energy storages.
also incorporate other energy supplies and demands, e.g. micro CHP plants, distributed generators of heat and power, and even electric vehicles depending on the local conditions. In this paper, we assume that heat is locally distributed according to the local heat demand and power is uploaded to the market. This is justified, since thermal energy is easier to store while electricity is more suitable for long-distance transmission. 2.2.2. The objective of the CHP-DH planning model For the abovementioned CHP-DH system, the energy production is primarily planned based on the heat demand, because the lack of heat is not permitted, and surplus heat must usually be disposed of at a cost, even though it can be partly stored in the TES. The objective of CHP-DH planning is to minimize the overall net acquisition cost for energy. The net acquisition cost includes production costs (fuel and other costs) subtracted by revenue from selling the generated electricity on the deregulated power market. The objective function can be written as
min Z ¼
" T X t¼1
X
! ctj xtj
#
ctp Pt
;
ð2Þ
j2Ju ;u2U
where Z is the net acquisition cost; T is the length of the planning horizon in hours; Ju is the set of extreme points of plants u; U is the set of all plants in the system; ctp and Pt are the electricity market price and the generated electricity in time step t. 2.2.3. Balances for heat, power and storages The heat balance of the CHP-DH system means that the district heat demand is satisfied by CHP and other heat production facilities including the TES. In this model, the charging and discharging efficiencies of the storages are taken into account. Eq. (3) is the heat balance for the CHP-DH system. Similarly, the power balance is written as in Eq. (4).
X q
j2J u ;u2U [U
X p
j2J u ;u2U [U
ðqj xj Þt qtchr þ gqdis qtdis ¼ Q t ;
ð3Þ
ðpj xj Þt ptchr þ gpdis ptdis ¼ Pt ;
ð4Þ
pq
pq
Here U is the set of all plants; Up, Uq, Upq are the sets of condensing power plants, heat plants and CHP plants respectively; qtchr, qtdis, ptchr, ptdis are the charging and discharging amounts of heat and power in time step t; gqdis, gpdis are the discharging efficiencies of heat and power; Qt is the heat demand in time step t. The solar heat production is modelled using EnergyPRO [29]. In the modelling, we assume that the initial storage level equals the storage level at the end of the planning horizon. Specifically, the thermal energy storage level at current time step t is affected by the storage level at previous time step (t 1) and the current charging and discharging heat from the storage. Therefore the storage efficiency [30] should also be taken into account besides charging or discharging efficiency [31,32]. The reason is that a small part of the previously stored heat will be lost due to the heat conductivity and heat radiation [31]. Therefore, the thermal energy storage balance can be expressed in Eq. (5), similarly the electricity storage balance can be written as in Eq. (6).
Stq ¼ gqs St1 þ gqchr qtchr qtdis ; q
ð5Þ
þ gpchr ptchr ptdis ; Stp ¼ gps St1 p
ð6Þ
Stq
Stp
where and are the storage level for heat and power at the end of time step t; gqs and gps are the storage efficiencies of heat and power; gqchr, gpchr are the charging efficiencies for heat and power. 2.2.4. Constraints for system control and operation The constraints for energy storage systems include: (1) the storage level constraints in Eqs. (7) and (8) which mean that the
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storage level should be in a range between the minimum and maximum for the safety and economy reasons; (2) the constraints of charging and discharging in Eqs. (9)–(12) indicating that the charging and discharging rates should be smaller than the corresponding maximum allowed rates for the economic and reliable operation of the CHP-DH system.
405
where q is the water density; V is the water volume of the TES; c is the specific heat capacity of water, c = 4.2 kJ/(kg °C); ts and tr are the supply and return water temperatures in the DH network. The convexity and non-negativity constraints for CHP are expressed in Eqs. (14) and (15). Non-negativity constraint for other plants is a little different and shown in Eq. (16).
X
xj ¼ 1;
ð14Þ
St;min 6 Stq 6 St;max ; q q
ð7Þ
Smin 6 Stp 6 Smax ; p p
ð8Þ
xj P 0; j 2 J u ; u 2 U pq ;
ð15Þ
0 6 qtchr 6 qt;max chr ;
ð9Þ
0 6 xj 6 1; j 2 J u ; u R U pq :
ð16Þ
t;max 0 6 qtdis 6 qdis ;
ð10Þ
t;max ; 0 6 ptchr 6 pchr
ð11Þ
0 6 ptdis 6 pt;max dis ;
ð12Þ
where Sqt;min , Sqt;max are the minimum and maximum heat storage max are the minimum and maximum eleclevels in time step t; Smin p , Sp t;max t;max are the maximum charging and districity storage levels; qchr , qdis max charging rates of TES in time step t; pmax chr , pdis are the maximum
charging and discharging rates of EES. The storage capacity of TES is influenced by the supply and return water temperatures in DH network. The storage capacity of TES equipped with unpressurized water tanks is calculated by Eq. (13), which considers water temperatures in the top and the bottom of the TES. A temperature difference of 5 degrees from the DH supply and return temperatures is considered in the calculation because of the temperature drop in the DH network. Moreover, a maximum temperature in the top level of is set 98 °C for an unpressurized TES.
Smax ¼ q
1 qVcfmin½ðts 5Þ; 98 ðtr þ 5Þg; 3600
ð13Þ
j2J u ;u2U pq
The power ramping constraint, which restricts how much the power production of a CHP plant may increase or decrease between two successive time steps is important in real-life operation of a CHP plant. In many cases, power ramping rate can be expressed as MW/h or sometimes can be the percentage of the power capacity of the plant. In this model, the power ramping constraint is,
jðpj xj Þtu ðpj xj Þt1 j 6 pu;ramp ; u
ð17Þ
or
jðpj xj Þtu ðpj xj Þt1 j=ðpj xj Þut1 6 eu;ramp ; u
ð18Þ
where pu,ramp is power ramping rate in MW/h, and eu,ramp is power ramping percentage. In addition, the maintenance constraint for CHP can be also considered in this model, because in some periods the CHP plant should be shut down for maintenance. In Eq. (19), the maintenance period is from time step Tm to Tm+n during the planning horizon.
xj ¼ 0; j 2 Ju ; t 2 ½T m ; T mþn :
ð19Þ
2.2.5. Solving the planning model The CHP-DH planning model (2)–(19) can be solved as an LP problem. In this model, the heat and power balances are taken into
Fig. 3. Schematic energy flow in a CHP-DH system with a solar thermal plant and a TES.
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account, and the energy balances as well as constraints for energy storages are considered. In addition, the power ramping constraint and maintenance constraint are also introduced to make the model more accurate. For a long time horizon, the model may become very large. In that case, it can be solved using special decomposition techniques, by solving hourly models separately and coordinated under top-level iteration to consider dynamic constraints [21]. In this study we have solved the model using LP2, which is based on the sparse revised Simplex and the product form of inverse [23,24]. LP2 can run in LP or MILP mode depending on the problem formulation. In this study, we use the LP mode in the following case study in order to reveal more details behind the algorithm and help understand the process of making an optimal planning and operation of such CHP-DH systems.
Table 2 Capacities and efficiencies of the three CHP plants. CHP plants
CHP 1
CHP 2
CHP 3
Capacity
12.5 MWe 38 MWth 97%a 87%a
11 MWe 36 MWth 85% 80%
8.4 MWe 17 MWth 85% 80%
High efficiency Low efficiency a
With flue gas heat recovery.
HOBs are also indicated and the efficiencies of the two HOBs are 90%. Generated electricity is transmitted to the market but heat is distributed to the local heat users and/or to the TES. Besides, the stored heat can be extracted when the heat demand is high but the heat production is insufficient. The charging and discharging rates of the TES are assumed half the storage capacity at each time step, which means that the TES can store or discharge at maximum half the capacity during one time step. In addition, the economic data related to the objective function in Eq. (2) are shown in Table 1. Other input data are described in the next section.
3. Case study: optimizing a CHP-DH system with a solar thermal plant and a TES 3.1. System description A CHP-DH system with a solar thermal plant equipped with solar panels and a heat storage using unpressurized water tanks is planned in a city community of south Finland. The schematic of the system is shown in Fig. 3 and it consists of,
3.2. Model input The most important data input for the CHP-DH planning model include the characteristic points of the three CHPs, the heat demand profile of the region, the market electricity price and solar radiation density. The characteristic points of the three CHPs are demonstrated in Fig. 4. Firstly, CHP plants cannot operate in the lower left corners of each plane. There is a minimum load for each CHP plant, if the actual load is smaller than the minimum load, then the CHP should be shut down. Secondly, for CHP 2 and CHP 3, only two characteristic points are available and they correspond
3 CHP plants, CHP 1 and CHP 2 are fed by biomass (wood chips) and CHP 3 by natural gas; 2 HOBs, HOB 1 fed by natural gas and HOB 2 by heavy oil; 1 solar thermal plant with 10,000 m2 of solar panels; 1 TES with 10,000 m3 water tanks. Fig. 3 shows the nominal fuel inputs, net electrical and heat outputs of the three CHP plants. Fuel inputs and heat loads of
Table 1 Economic data of the CHP-DH system planning model. Fuel prices (€/MW h)
a b c
Fuel taxes (€/MW h)
Efficiencies of TES
Power ramping
Wood
Natural gas
Oil
Wood
Natural gas
Oil
Charging
Storage
Discharging
21
40
100a
0
13.664b
10.199b
0.95
0.998c
0.95
10%
Estimated oil price based on the market prices; all prices are deemed stable during the planning period. Starting at year 2015. Estimated at U value 0.2 W/(m2 K), average temperatures in and out of the tanks 60 °C and 3 °C.
(a) CHP 1 – biomass
(b) CHP 2 – biomass
(c) CHP 3 – natural gas
Fig. 4. Characteristic points of the three CHP plants (f = fuel, p = power, q = heat). (a) CHP 1 – biomass, (b) CHP 2 – biomass, (c) CHP 3 – natural gas.
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Fig. 5. Heat demand profile of the region in January and July, 2011.
Fig. 6. Hourly spot electricity price of the region in January and July, 2011.
Fig. 7. Hourly solar radiation density of the region in January and July, 2011.
Table 3 System components of the CHP-DH system scenarios. Scenario
1 2 3 4
CHP
HOB
CHP 1
CHP 2
CHP 3
HOB 1
HOB2
d d d
d d d
d d d
d d d d
d d d d
Solar thermal plant
TES
DH load
Electricity market
d d d d
d d d d
d d d d
d d d d
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
CHP 2
CHP 3
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(a) scenario 1 CHP 2
CHP 3
HOB 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(b) scenario 2 CHP 1
CHP 3
HOB 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
672
720
Time (h)
(c) scenario 3 CHP 1
CHP 2
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0 48
96
144
192
240
288
336
384
432
480
528
576
624
Time (h)
(d) scenario 4 Fig. 8. Heat production of the CHP-DH system vs. heat demand in January.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
30
CHP 2
CHP 3
Power (MW)
25 20 15 10 5 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
528
576
624
672
720
528
576
624
672
720
528
576
624
672
720
Time (h)
(a) scenario 1 CHP 2
30
CHP 3
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(b) scenario 2 CHP 1
30
CHP 3
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(c) scenario 3 CHP 1
30
CHP 2
Power (MW)
25 20 15 10 5 0
48
96
144
192
240
288
336
384
432
480
Time (h)
(d) scenario 4 Fig. 9. Power generation of the CHP-DH system in January.
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to the two extreme working points. However, we obtained a third characteristic point for CHP 1 based on analyzing the real-life operating records. Thirdly, the variation of efficiency due to the load changing of CHP is taken into account in the characteristic points and operating region. Rankine cycle is used in two biomass CHP plants, therefore their characteristic curves are almost the same; however biomass CHP 1 has a little higher efficiency because it is a new plant and the waste heat in flue gas is also recovered for DH. In CHP 3, an open-cycle gas turbine with heat recovery boiler is used. Table 2 shows the capacities and efficiencies of the three CHP plants. The heat demand profile of this region during 2011 is obtained and shown in Fig. 5. The profile includes the heat demand for space heating and for heating the tap water. In addition, the outdoor temperature, supply and return temperatures of the DH network are also obtained and used to calculate the capacity of the TES. The spot electricity prices and the solar radiation density of this region are shown in Figs. 6 and 7. We can find that the electricity prices in winter is a little higher than in summer, and the hourly variations of the prices are very clear. As can be seen from Fig. 7, the solar radiation density is rather low in winter time in Finland, but very high in summer time. 3.3. Results The data of January and July were used to demonstrate the developed model and to optimize the planning and operation of the CHP-DH system. According to the system components shown in Fig. 3, there are three CHP plants and two HOBs in this CHPDH system, but it is found that the district heat demand is smaller than the heat outputs altogether. This means that all the three CHP plants are not necessarily needed at the same time for DH. Therefore, the CHP-DH scenarios studied in this paper are shown in Table 3, where the cross means the corresponding CHP is not included. We can optimize the system using the MILP mode of LP2, but LP mode is used in this study in order to reveal more details behind the algorithm and help understand the process to make the optimal planning and operation of the CHP-DH system. In the following, the results of January and July are demonstrated consecutively. Fig. 8 shows the heat production vs. heat demand for each CHPDH system scenario in January, 2011. We find that all CHP-DH systems produce heat well according to the heat demand, but the HOBs are not needed in scenarios 1 and 4. In scenario 1 shown in
Fig. 8(a), CHP 3 (fed by natural gas) is kept running at minimum load due to the high fuel cost, and CHP 1 and 2 often operate at partial loads in order not to produce surplus heat. In scenario 4 shown in Fig. 8(d), two biomass CHP plants (1 and 2) will coordinate with each other to satisfy the heat demand, and they need to operate in partial loads frequently but the average loads are a little higher than that in scenario 1. As can be seen from Fig. 8(b) and (c), if the gas-fired CHP 3 is planned with either CHP 2 or CHP 1, then the biomass CHP will operate at full load in the whole month, and CHP 3 operate in full load when heat demand is high; however when they are still insufficient, HOB 1 will be activated for flexible peak heating, which can follow the heat demand accurately. In addition, the heat output from the solar thermal plant is rather small, because of the extremely low solar radiation density in Finland in January. It can also be found that HOB 2 (fed by oil) is not necessarily needed even in the severe cold period under the current system configuration and heat demand profile. Fig. 9 shows the power generation of each CHP-DH scenario in January, 2011. It can be found that the power generation is highly coupled with heat production of CHP plants. The total fuel consumption and gross electricity generation of all CHP-DH scenarios are shown in Fig. 10, in which we find that the maximum difference of gross electricity generation is only 6% between scenarios 2 and 3. CHP plants in Scenario 2 generates less electricity and thus less heat, therefore HOB 1 will produce more heat, which is justified by the fuel consumption of HOB 1 in Fig. 10. Even all three CHP plants are running in scenario 1, the gross electricity generation is less than scenario 3 consisting of only CHP 1 and 3. In addition, scenario 4 with two biomass CHP plants can have a more efficient combined heat and power production than that in scenario 1 due to a higher average CHP load. These also indicates that the three CHP plants are not planning efficiently in scenario 1. The fuel consumption of CHP plants and HOBs are shown in Fig. 11. It can be seen from Fig. 11(a) that CHP 3 is running with minimum load and other CHP plants often operates with fluctuating partial loads. One way to improve this is to get more characteristic points, but it is better to remove one CHP plant from the analysis according to the current heat demand profile and to make the rest CHP plants operate more efficiently. This improvement can be observed in Fig. 11(b) and (c), where the biomass CHP (1 or 2) is always running in the design condition as the base plant, and the other CHP operates with high average load. In the last scenario, two biomass CHP plants are also running under high average loads but fuel consumption is much less without the need of gas.
Fig. 10. Total fuel consumption and gross electricity generation of the CHP-DH scenarios in January.
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Fuel consumption (MW)
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(d) scenario 4 Fig. 11. Fuel consumption of the CHP plants and the HOB in January.
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(d) scenario 4 Fig. 12. Charging and discharging heat load of the thermal energy storage in January.
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Heat storage (MWh)
450 Heat storage capacity
400 350 300 250 200 150
Heat storage level
100 50 0 48
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Time (h)
(a) scenario 1 500
Heat storage (MWh)
450 400
Heat storage capacity
350 300 250 200 150 Heat storage level
100 50 0 48
96
144
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Time (h)
(b) scenario 2 500
Heat storage (MWh)
450 Heat storage capacity
400 350 300 250 200
Heat storage level
150 100 50 0 48
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Time (h)
(c) scenario 3 500
Heat storage (MWh)
450 400
Heat storage capacity
350 300 250 200
Heat storage level
150 100 50 0 48
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Time (h)
(d) scenario 4 Fig. 13. Heat storage capacity and storage levels in January.
The charging and discharging heat loads of the TES are shown in Fig. 12. We conclude from Fig. 12(a) and (d) that the TES is used more frequently if CHP 1 and 2 are considered in the
modelling (scenarios 1 and 4), this is because CHP 1 and 2 often operate in partial loads which fluctuate very sharply and no one can be the base plant in most of the planning horizon. Therefore
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Fig. 14. Objective function of the CHP-DH scenarios in January.
the TES is often used to balance the heat production and the heat demand. However, in other two scenarios, one of the biomass CHP plants (1 or 2) is running as the base plant, HOB1 is utilized as the peak boiler [33], which produces district heat to balance the heat production and the heat demand, therefore the use of TES is less. The heat storage levels are illustrated in Fig. 13. In this study, we use 99.8% hourly storage efficiency to optimize the CHP-DH system. The result indicates that TES is used more often and the storage level is generally growing higher when the loads of CHP plants are fluctuating, because more storage will be needed to balance the heat production and heat demand. The objective function in Eq. (2) which is defined as the total cost of net acquisition is shown in Fig. 14. We can see that the values of objective function are so different. Scenario 4 with two biomass CHP plants has the smallest cost and scenario 2 is most expensive, which is about 170% larger than that of scenario 4. This is because of the high fuel cost of natural gas consumed in CHP 3 and HOB 1. In addition, the costs of scenarios 1 and 3 are about 43% and 119% larger than that of scenario 4 respectively. Therefore, the CHP-DH scenario 4 with two biomass CHP plants is more preferred under the current system configuration and heat demand profile. For CHP-DH scenario 4, the optimization results in July, 2011 are shown in Fig. 15. It is clear that the heat load is much lower, and thus one of the biomass CHP plants (CHP 2) is shut down. During this period the service and maintenance can be implemented. The solar thermal plant produces more renewable heat thanks to the high solar radiation density in July. The power generation is also coupled with the heat production, and the power ramping constraint (10%) is also satisfied as shown in Fig. 15(b). Note that the heat storage capacity is much lower than that in January and the use of TES is much less than that in January, because CHP 1 operates smoothly near the minimum load. Finally, Fig. 16 shows the thermal storage percentage of TES at each time step during January and July. Thermal storage percentage is defined as the ratio between heat storage level and the heat storage capacity at each time step. We found that both the heat storage level and storage capacity are fluctuating hourly, therefore storage percentage is defined to indicate the use of the TES. It can be found that thermal storage percentage in January is higher than
in July, but sometimes it is not small in summer. There are two reasons for this: (1) although the CHP is running at minimum load, there is surplus heat due to the low heat demand and high solar thermal output in July; (2) the storage capacity is smaller in July due to the low DH temperature as shown in Fig. 15(e), and thus more TES volume will be needed in July to store a certain amount of heat than in January. 3.4. Discussion – future situation with a higher share of RES and a larger TES The above analyses are based on a real case having a small solar heat plant and a TES. However, more solar panels and heat storage tanks can be built in the future to meet the climate change target. Therefore, in order to simulate the future situation of the CHP-DH system with a higher share of solar heating and a larger TES, scenarios 5 and 6 shown in Table 4 are studied. In the two future scenarios, the areas of solar panels are either two or four times bigger than that in the current real case and the TES volume is also doubled. Meanwhile, the DH load is assumed to increase 20%; DH load increases slowly because the buildings are becoming more energy efficient in the future. 3.4.1. Heat production Heat production vs. heat demand in January and July for the two future scenarios are shown in Fig. 17. It can be seen that even the capacity of solar thermal plant is much bigger, solar heating still contributes only 0.1–0.2% to the DH in January, but in July the share of solar heat can reach 15.4–30.7%. In addition, the bigger TES enable the biomass CHP plants to operate as smoothly as possible with high average loads compared with Fig. 8(d). 3.4.2. The usage of TES The usage of TES is demonstrated in Figs. 18 and 19. As can be seen from Fig. 18, the TES will be used more extensively in January than in July, and the charging and discharging loads are larger than the current case of scenario 4, shown in Fig. 12(d). In addition, Fig. 19(a) and (b) show that the TES is fully used sometimes by storing the surplus heat from CHP so that CHP plants can operate with high average loads to increase the production efficiency. According to Fig. 19(c) and (d), the TES capacity is smaller in July.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
Solar thermal plant
Heat demand
100 90
Heat load (MW)
80 70 60 50 40 30 20 10 0
48
96
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Time (h)
(a) scenario 4 - heat production v.s. heat demand in July CHP 1
30
Power (MW)
Power (MW)
25 20 15
6 5 4 3 2 1 0 260
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Time (h)
10 5 0
48
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Time (h)
Fuel consumption (MW)
(b) scenario 4 - power generation CHP 1
60 55 50 45 40 35 30 25 20 15 10 5 0 48
96
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288
336
384
432
480
528
Time (h)
Charging or discharging heat load (MW)
(c) scenario 4 - fuel consumption Charging heat load
25
Discharging heat load
20 15 10 5 0 -5 -10 -15 -20 -25 48
96
144
192
240
288
336
384
432
480
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Time (h)
(d) scenario 4 - charging and discharging heat load Fig. 15. CHP-DH system optimization results in July.
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Heat storage (MWh)
450 400 350 300
Heat storage capacity
250 200 150 100 Heat storage level
50 0 48
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Time (h)
(e) scenario 4 - heat storage capacity and storage levels Fig. 15 (continued)
Fig. 16. Storage percentage of the TES in January and July.
Table 4 System components of the future CHP-DH scenarios. Scenario
5 6
CHP CHP 1
CHP 2
d d
d d
HOB1
Solar thermal plant
TES
DH load
Electricity market
d d
d (20,000 m2) d (40,000 m2)
d (20,000 m3) d (20,000 m3)
d (20% increase) d (20% increase)
d d
We observe that if the solar panels are increased to 40,000 m2, then CHP 1 will operate more smoothly near the minimum load and thus the TES will be used less often, but charging and discharging loads are a little bigger, finally resulting in smaller thermal storage percentage. 3.4.3. Fuel consumption, power generation and objective function The total fuel consumption and gross electricity generation of the future scenarios are shown in Table 5. We conclude that the bigger solar thermal plant can reduce the fuel consumption of the CHP-DH system, but not too much because the CHP plants still have to generate a certain amount of electricity and make profit so that the total cost of acquisition in Eq. (2) can be minimized.
However, more fuel can be saved if the HOB supplies a bigger proportion of the heat demand in a CHP-DH system. In Fig. 20, the objective function – total cost of acquisition are compared between the current case and the future scenarios. Note that in the two future scenarios, DH load is increased by 20%. It can be found that in January, the objective functions of scenarios 5 and 6 are greater than that of the current case, because of the increased heat demand and low contribution from solar. The objective function of scenario 6 is a little smaller than that of scenario 5 thanks to the slightly higher share of solar heat. The objective function of the two future scenarios in July are almost the same with current case, that is to say, the increased 20% of DH load can be satisfied by the solar heating.
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H. Wang et al. / Applied Energy 159 (2015) 401–421 CHP 1
CHP 2
HOB 1
Solar (20000m2)
Heat demand increased by 20%
120 110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
96
144
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336
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432
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720
Time (h)
(a) scenario 5 - January CHP 1
CHP 2
HOB 1
Solar (40000m2)
Heat demand increased by 20%
120 110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
96
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(b) scenario 6 – January CHP 1
120
Solar (20000m2)
Heat demand increased by 20%
110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
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Time (h)
(c) scenario 5 - July CHP 1
120
Solar (40000m2)
Heat demand increased by 20%
110 100
Heat load (MW)
90 80 70 60 50 40 30 20 10 0
48
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Time (h)
(d) scenario 6 - July Fig. 17. Heat production vs. heat demand in January and July for future scenarios 5 and 6.
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Charging or discharging heat load (MW)
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Discharging heat load - January
Charging heat load (TES=20000m3, Solar panel=20000m2) - January
40 30 20 10 0 -10 -20 -30 -40
48
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Time (h)
Charging or discharging heat load (MW)
(a) scenario 5 - January 40
Charging heat load (TES=20000m3, Solar panel=40000m2) - January
Discharging heat load - January
30 20 10 0 -10 -20 -30 -40 48
96
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Time (h)
Charging or discharging heat load (MW)
(b) scenario 6 – January 40
Charging heat load (TES=20000m3, Solar panel=20000m2) - July
Discharging heat load - July
30 20 10 0 -10 -20 -30 -40 48
96
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Time (h)
Charging or discharging heat load (MW)
(c) scenario 5 - July 40
Charging heat load (TES=20000m3, Solar panel=40000m2)- July
Discharging heat load - July
30 20 10 0 -10 -20 -30 -40 48
96
144
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336
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Time (h)
(d) scenario 6 - July Fig. 18. Charging and discharging heat load of the thermal energy storage in January for the future scenarios.
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heat storage level - January
1000
Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
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Time (h)
(a) scenario 5 - January Heat storage capacity (TES=20000m3, Solar panel=40000m2) - January
heat storage level - January
1000
Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
96
144
192
240
288
336
384
432
480
528
576
624
672
720
Time (h)
(b) scenario 6 – January Heat storage capacity (TES=20000m3, Solar panel=20000m2) - July
heat storage level - July
1000
Heat storage (MWh)
900 800 700 600 Heat storage capacity
500 400 300 200 100 0 48
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Time (h)
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heat storage level - July
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Heat storage (MWh)
900 800 700 600 500 400 300 200 100 0 48
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Time (h)
(d) scenario 6 - July Fig. 19. Heat storage capacity and storage levels in January for the future scenarios.
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Table 5 Total fuel consumption and gross electricity generation of the future CHP-DH scenarios (MW h). Scenario – month
Scenario Scenario Scenario Scenario
5 6 5 6
– – – –
January January July July
Fuel consumption
Gross electricity generation
CHP 1
CHP 2
HOB 1
37,325 37,285 11,466 11,209
39,163 39,108 – –
1683 1670 – –
16,816 16,809 2326 2247
Fig. 20. Comparison of the objective function for current case and future scenarios.
4. Conclusions
References
Renewable energy sources (RES) will be used more extensively in the future combined heat and power based district heating (CHP-DH) systems. In this paper, a CHP-DH system with RES and energy storage system (ESS) is proposed to promote the use of RES and to approach the climate change mitigation target in the context of secured energy supply and scarce resources. A modelling and optimization method was developed for the CHP-DH system in order to operate the system optimally. The model takes into account the energy balances, power and heat. To make the model more accurate and realistic, constraints on energy storage and the power ramping are also included. This large CHP-DH planning problem was solved using an efficient LP solver. The modelling and optimization method was demonstrated with a CHPDH system with a solar thermal plant and a thermal energy storage (TES). The same system was also optimized with a higher share of RES and a larger TES in order to simulate the future situation. The results indicate that the developed modelling and optimization method is efficient and flexible for planning and operating the CHP-DH systems. It can also be used for optimizing the combination of system components and the sizing problems in the future. It was found that the storage efficiencies should be well considered when optimizing the operation of the CHP-DH system. The optimal operation of the TES is influenced by both the heat demand and power price. The TES is used more intensively in the future with a more fluctuating CHP load and a higher share of RES.
[1] International Energy Agency. Renewable energy medium-term market report 2014 – market analysis and forecast to 2020. Paris, France; 2014. [2] World Wide Fund for nature. The energy report – 100% renewable energy by 2050. Gland, Switzerland; 2011. [3] Eurostat. Share of renewable energy up to 14.1% of energy consumption in the EU28 in 2012; 11 March 2014. [4] International Energy Agency. Energy technology perspectives 2012 – pathways to a clean energy system. Paris, France. [5] Jiang Y. Development trend of building energy conservation and key points.
Acknowledgments This study is funded by project STEEM (Sustainable Transition of European Energy Market) Aalto University, Finland and by the Twelfth Five-Year-Plan of China under grant number 2012BAJ04B01.
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