A study on operation control of urban centralized heating system based on cyber-physical systems

Journal Pre-proof A study on operation control of urban centralized heating system based on cyberphysical systems Xiaojie Lin, Sibin Liu, Shuowei Lu, Zhongbo Li, Yi Zhou, Zitao Yu, Wei Zhong PII:

S0360-5442(19)32264-9

DOI:

https://doi.org/10.1016/j.energy.2019.116569

Reference:

EGY 116569

To appear in:

Energy

Received Date: 5 June 2019 Revised Date:

15 November 2019

Accepted Date: 16 November 2019

Please cite this article as: Lin X, Liu S, Lu S, Li Z, Zhou Y, Yu Z, Zhong W, A study on operation control of urban centralized heating system based on cyber-physical systems, Energy (2019), doi: https:// doi.org/10.1016/j.energy.2019.116569. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

A Study on Operation Control of Urban Centralized Heating System Based on Cyber-Physical Systems Xiaojie Lin1, Sibin Liu1, Shuowei Lu1, Zhongbo Li1,2, Yi Zhou1, Zitao Yu1, Wei Zhong1* 1

Institute of Thermal Science and Power Systems,

College of Energy Engineering, Zhejiang University, Hangzhou, China 2

Beijing District Heating Group, Beijing, China

*: Corresponding Author, Tel: +86 13989882228, E-mail: [email protected]

ABSTRACT Intelligent and efficient operation of large-scale urban centralized heating system (UCHS) is a hot topic in urban energy system field. This study develops an intelligent operation control platform for China’s UCHS via “cyber-physical systems” (CPS). The platform is based on graph theory and thermal-hydraulic modeling and has been validated against measured data collected from the substations. 88.5% of the modeled substation supply pressure has an error of less than 5%, and 82.1% of the modeled substation return pressure has an error of less than 5%. The platform carries out “model-based prediction” and “prediction-based decision-making” by incorporating state sensing, load forecasting, modeling, and model-based operation optimization. The platform is further applied to a target UCHS using coal-based combined heat and power (CHP) units and gas-fired boilers as heating sources and covering an area of 15 million m2. To ensure the accuracy, this study carries out online calibration with real-time operation data and tests the load distribution feature during the pilot heating season. During a two-day operation, the optimized heating sources load distribution scheme could reduce natural gas consumption by up to 31.2% when compared with existing experience-based operation. The operation cost during that operation period is also reduced by 2.6%, accordingly. Keywords: Cyber-Physical Systems; Urban Centralized Heating System; Operation Control

1

NOMENCLATURE Symbols F

mass or energy

t

time

N

set of nodes

I

total number of nodes

S

set of sections

J

total number of sections

C

set of circuits

K

total number of circuits

U

connection matrix of nodes and sections

W

connection matrix of sections and circuits

G

column vector of net mass flow rate of nodes (kg/s)

m

column vector of mass flow rate of sections (kg/s)

∆P

column vector of pressure loss of sections (Pa)

∆Pfri

friction resistance pressure loss (Pa)

∆Ploc

local resistance pressure loss (Pa)

∆Pdyn

dynamic pressure loss (Pa)

∆Pgra

gravitational pressure loss (Pa)

λ

friction resistance coefficient

l

length (m)

d

pipe diameter (m)

ξ

local resistance coefficient

g

gravitational acceleration (m/s2)

E

flow resistance factor

Qnode

column vector of net heat transfer of nodes (kJ/s)

2

Qsection

column vector of heat loss of sections (kJ/s)

cTavg

specific heat of fluid under the average temperature (kJ/kg K)

∆T

temperature difference between both ends of sections ( )

k

heat transfer coefficient (W/m2 K)

R

thermal resistance (m2 K/W)

ε

iteration parameter

PARA

parameter to be calibrated

A

total number of sets of measuring conditions for model calibration

PND

total number of measuring points of node pressure

PS

model simulation result of node pressure (Pa)

PM

measured data of node pressure (Pa)

MSEC

total number of measuring points of section mass flow rate

MS

model simulation result of section mass flow rate (kg/s)

MM

measured data of section mass flow rate (kg/s)

TND

total number of measuring points of node temperature

TS

model simulation result of node temperature ( )

TM

measured data of node temperature (

X

normal variable

BO

total number of gas-fired boilers

CHP

total number of coal-based CHP units

HS

total number of all heating sources units

F(Q)

operation costs for the heating source unit to supply heat Q

E(Q)

pollute emissions from the heating source unit to supply heat Q

δ

emission penalty price

L

emission limits

3

)

Acronyms CHP

combined heat and power

CPS

cyber-physical system

IoT

internet of things

SCADA

supervisory control and data acquisition

UCHS

urban centralized heating system

Subscript in

inlet/ inner wall

out

outlet/ outer layer

i

serial number of nodes/ gas-fired boilers/ measuring points of pressure

j

serial number of sections/ coal-based CHP units/ measuring points of mass flow rate

k

serial number of circuits/ heating sources units/ measuring points of temperature

p

pipe wall

isu

insulation layer

amb

ambience

bo

gas-fired boiler

chp

CHP unit

bl

branch line

set

preset value

4

1. Introduction 1.1

Background

In China, the urban resident population had already reached 790 million by the end of 2016, making up 57.35% of the total population. A top priority of the Chinese government is to improve the planning and management of these highly-populated cities through so-called “Smart City” initiatives. Urban centralized heating system (UCHS) is the keystone of “Smart City” as it creates essential energy infrastructure to support economic development and ensures the livelihood of the residents. Along with the rapid urbanization, the scale of UCHS in China keeps expanding. In 2015, the overall length of the pipe network exceeded 200,000 km and the covered heating area reached 8,500 million m2 [1]. Centralized heating systems of megacities could cover tens to hundreds of millions of square meters. For example, the centralized heating system in Beijing covers a floor area of more than 600 million m2. It is extremely challenging to ensure safe, reliable, energy-saving and environmentally benign operation of these heating systems. According to Fang et al. [2], urban and rural heating in northern China are dominated by combined heat and power (CHP) plants and coal-based boilers while the coal-based boilers accounted for 81% of the total heating area. Zhang et al. [3] reported that half of the coal is consumed through low-efficiency approach and found that pollutant emission from the inefficient combustion of coal is the main cause of fog and haze in northern China. During the last decade, the researchers have observed the following trends in UCHS in developing countries such as China. •

It has an increasing percentage of clean coal-based CHP plants as the major heating sources while adopting renewable-energy-based heating sources. With the application of renewable-energybased heating sources, the issue will be how to achieve the decoupled operation of heat and power for existing CHP plants, as shown by Wang et al. [4].

•

The diversity of heating sources requires interconnection of multiple heating networks. The issue will be how to ensure the reliability and maintain the balance in UCHS through operation optimization of multi-source and multi-energy complementary heating systems, as found by Dimoulkas et al. [5].

•

With the development of internet of things (IoT) technology and the heat metering project, the UCHS has met with the basic automation requirements for precise heat supply. The critical issue will be how to realize energy saving by promoting heating demand-side response.

Overall, the operation control of UCHS with an ever-increasing scale of population or covered area is challenging. One prerequisite of approaching this challenge is to develop the relevant models of systems 5

which could be used in load prediction and operation optimization. In this area, the modeling of conventional regional heating system is close to the that of district-level energy systems. For example, Gabrielaitiene et al. [6] developed a full-scale model for a six-megawatt district heating system in Denmark with a total heating network pipe length of five kilometers and analyzed the results with the commercial software TERMIS. As for looped structure case, Vesterlund and Dahl [7] developed a district heating system model based on meshed approach to specifically deal with a community-level multisource looped heating network and visualized the heat transport bottlenecks of the network. Allegrini et al. reviewed and summarized the modeling approach and corresponding software tools in district-level energy systems, including district heating systems and multi-energy systems[8]. Although researchers have highlighted the impact of renewable-energy-based heating sources and the necessity of high-efficiency heating network, the corresponding technologies (especially the operation control technologies) required to achieve such stable and high-efficiency operation are less discussed when compared to the modeling work. Existing operation control studies less consider the operation control of UCHS with increasing scale and a variety of heating sources such as renewable-energy-based heating ones. For example, Lund et al. [9] reviewed the current “Fourth Generation District Heating” widely used in Nordic countries and found that the core ideas were the combination of low-temperature heating networks, renewable-energy-based heating sources, waste heat recovery, and the so-called “smart energy systems.” Østergaard and Lund [10] developed a technical scenario for the transition of energy supply from being predominantly supported by fossil fuel to locally available renewable-energy-based sources. Utlu et al. [11] presented an experimental thermal investigation of a hybrid renewable heating system and analyzed the exergy efficiency by using real data obtained from a prototype structure. One of the difficulties of utilizing renewable-energy-based sources, together with conventional ones is the operation control. For example, He et al. [12] discussed the volatility and uncertainty of intermittent renewable energy and proposed to use optimal planning model to optimize the scale of the clean heating and capacity of electric heating devices as well as thermal storage devices. In this area, modeling and optimization play critical roles in solving the coordination of renewable and conventional heating sources. For example, Wang et al. [4] studied a CHP-based district heating system with renewable-energy-based sources and energy storage devices by developing its modeling and optimization method used for planning and operation. Dimoulkas et al. [5] proposed a short-term operation model of a district heating system to optimally schedule the production of heat and power in a system with high wind power penetration. Vigrants and Blumberga [13] developed a district heating system calculation model to calculate the optimal flow and temperature design conditions in heating network.

6

Researchers also tested the modeling-based coordination of renewable and conventional heating sources on a prototype or small-scale heating system. For example, Wanjiru et al. [14] developed a closed-loop model predictive control to operate a heat pump water heaters and instantaneous showers used in zeroenergy buildings. Nikula et al. [15] proposed a co-simulation environment for a dynamic process simulator and an event-based control system by demonstrating the effectiveness of the solution with a case study of a district heating network. Åberg et al. [16] constructed a cost-optimization model of a district heating system by using tool MODEST and discussed scenarios with heat demand changes due to increased energy efficiency in buildings through sensitivity analysis of electricity price variations. Nielsen and Madsen [17] found that grey-box modeling made up of an initial model structure and data of heat consumption and climate (temperature, wind speed and global radiation) is powerful for on-line prediction of heat consumption. Ferraty et al. [18] introduced a peak load forecasting methodology in a district heating system based on functional regression approach which can support predictive control. Ganchev et al. [19] presented the design and realization of an IoT-based smart electric heating control system. Tunzi et al.[20] simulated small-scale district heat networks based on TERMIS and presented the influence of water supply and return temperature optimization technique on its overall performance. Overall, the representative existing studies in operation control of UCHS (especially multi-sources ones) or like could be summarized in Table 1.

7

Table 1 Summary of Representative Existing Studies in Modeling and Operation Control of UCHS Authors

Research Focus

Highlights

Gabrielaitiene et al. [6]

District heating system

Full-scale model validation and comparison with commercial tools

Vesterlund and Dahl [7]

District heating system

Meshed model approach and transport bottleneck diagnosis

Vigrants and Blumberga [13]

District heating system

Optimal temperature and flow rate distribution calculation

Nikula et al. [15]

District heating system

Co-simulation of process and eventbased control logic

Åberg et al. [16]

District heating system

Sensitivity study of electricity price variation

Nielsen and Madsen [17]

District heating system

Grey-box simulation prediction

Ferraty et al. [18]

District heating system

Peak load prediction and predictive control

Ganchev et al. [19]

District heating system

Smart heating based on IoT and electric heating

Tunzi et al.[20]

District heating system

Water supply and return temperature distribution optimization

Wanjiru et al. [14]

Heat pump heating and zero-energy Close-loop model predictive control buildings

Lund et al. [9]; Østergaard and Lund [10]

“Fourth Generation District Low-temperature heating operation; Heating”; Transition of heating Utilizing local renewable heating sources sources

Utlu et al. [11]

Hybrid renewable heating system

He et al. [12]

Renewable energy and storage Optimal planning coordination considered

Wang et al. [4]

Combination of CHP plants, Optimal planning and operation renewable sources and storage

Dimoulkas et al. [5]

District heating system and wind Short-term operation and dispatch power penetration

8

and

online-

Exergy analysis with

volatility

In addition to the studies reviewed above, novel operation control concepts such as cyber-physical systems (CPS) is less considered in the area of complex energy system (such as UCHS) control. CPS is based on new-generation information technologies such as IoT, big data, cloud computing, and artificial intelligence (AI). It can solve the dynamic resource allocation and optimal control problem of complex systems. In terms of applying CPS to energy systems, Yu and Xue[21] discussed the potential impact that CPS can have on smart grids. Rajkumar et al. [22] discussed the possibility of applying CPS to areas such as energy, transportation, health-care, manufacturing, and agriculture by discussing the challenges of developing a fully-integrated, robust, and failure-free model with distributed interacting and real-time control features through cyber and physical approach with a special interest on the power grid. Faruque and Ahourai’s study [23] demonstrated a model-based design method for creating a “Cyber-Physical Energy System” based on residential microgrid. Ramos et al. [24] described the use of intelligent supervisory control and data acquisition (SCADA) systems of CPS at different levels of the power system. Liu et al. [25] analyzed the basic concepts and research status of CPS for the power grid and presented the research framework which was made up of four key techniques. During the application of CPS concept, researchers found the advantages of CPS in improving user behavior and achieving demand response. For example, Chen and Luo [26] showed that CPS-based smart grid with the function of sensing-driven predictions and optimization-based purchase decision making could cope well with uncertainties in demand, supply, and electricity prices. Maasoumy [27] presented a framework and the control mechanism for smart building in the context of the smart grid that could take advantage of the flexibility of HVAC systems. As the dynamic and complexity of all aspects of UCHS (source-network-load-storage) expand dramatically, the operation control of large-scale heating systems (especially the large-scale multi-source interconnected ones) becomes a critical issue. However, the study of applying CPS to large-scale energy systems (outside area of electricity) is relatively rare. 1.2

Motivation

In the area of UCHS, existing studies focus either on the design and test of feedback or more advanced controllers or on the analysis of demand-side user patterns. However, less attention is paid to novel concepts such as CPS and its potential to be used in the operation control of UCHS. This paper lays out the overall CPS-based technology roadmap of an operation control platform of UCHS. After that, this paper will show the key elements of the developed CPS-based control platform in a northeastern China city and the validation results. The paper will then show the reduction of energy consumption and cost during a pilot operation through the optimized load distribution among multiple sources by the developed CPS-based heating control platform. 9

2. CPS-Based Operation Control Platform 2.1

Technology Roadmap

UCHS in China usually uses hot water as the working medium. A typical UCHS consists of dozens of geographically distributed heating sources generating hot water, a primary heating network distributing hot water to hundreds (or more) heating substations, and a secondary heating network delivering hot water to buildings. During the operation, heating sources generate high-temperature water circulating in the primary loop. The pumps in the loop subsequently drive the hot water to the heating substations. In the heating substations, the water from the primary loop heats the water circulating in the secondary loop through a plate heat exchanger. The pumps in the secondary loop thereafter deliver hot water to the endusers (buildings). The difficulties of operation control of UCHS come from the following aspects: •

Difficulty of coordinated control among multiple heating sources with different characteristics. The introduction of renewable-energy-based heating sources amplifies the uncertainty of heating control.

•

Thermal inertia and the corresponding system response delay. Thermal inertia aggravates the impact of uncertainty in the supply and demand side.

•

Coupling effect of topological structures (pumps, valves) of the heating network. This effect leads to a hydraulic imbalance of the system.

•

Lack of measurement data on heat demand side and feedback controllers.

Due to these problems, the conventional UCHS system is slow in finding a new operating point under scenarios other than the design conditions. Once the renewable-energy-based heating sources are introduced, the existing controllers could not find the operation scheme that could meet the delicate demands on the end-user side, as is found in China’s rapid urbanization. The CPS concept provides new insight into these challenges, and it is possible to sort out a new technology roadmap of the operation control platform to solve these problems, as shown in Figure 1. The core idea is “predictive analysis based on heating system model” and “optimal control decision-making based on predictive analysis.” The essential methodology is to combine the automation system and the IoT technology to exchange state sensing and action execution information between the physical (UCHS) system and the cyber (platform) system. The key to optimizing the operation control scheme is to introduce an effective method based on predictive analysis, which could deal with the thermal inertia and coupling within large-scale UCHS.

10

Figure 1 Structure of UCHS and CPS-Based Control In practice, the CPS-based control platform takes the demand of heating substations as inputs to calculate target pressure and temperature distribution satisfying specific operation target (emission, thermal comfort and so on) in the heating network. The heating system model in control platform reflecting the complex coupling relationship between heating substations and gives out the pressure and temperature in both the supply and return nodes of the networks under the target distribution. Data modeling and identification method further convert this information to a set of operation actions of heating sources, pumps and valves. The process repeats itself in an iterative manner, and the UCHS undergoes a series of predictive operation. 2.2

Framework of CPS-Based Control Platform

Based on the technology map, framework for a CPS-based control platform is also developed in this study, as shown in Figure 2. It includes parts such as state sensing, control execution, heat load forecasting, modeling simulation, and multi-sources load distribution. These parts are mutually supportive, and they constitute a technical system covering the whole “source-network-load-storage” process in UCHS.

11

Figure 2 Framework of CPS-Based UCHS Control Platform Among this methodology, the heating system model is the core of the control platform. It is the key to the construction of the simulator of the physical system. It describes the complex coupling relationship within the heating system quantitatively. The steady-state models of the heating system are built based on thermal-hydraulic laws and the parameters of the model can be calibrated by data-driven approach. Based on the model of the heating system and the real-time data, the researchers could further investigate the operation state and evaluate the corresponding safety margin. The model specifies the operation point while the measured data, in its scattered form, depicts the real-time status of the physical heating system around the operation point. It is necessary to carry out a state analysis and estimation through so-called “soft measurement” process. Furthermore, based on the prediction and analysis capability of the heating system model, researchers could have the platform infer the optimized control decisions via heuristic optimization algorithm. This decision-making process could be applied to realize multi-sources load distribution, operation control of heating sources, flexible mass and heat transport control of heating networks, emergency plan analysis and so on. In a word, the closed-loop control platform of “state sensing - model simulation - optimization decision accurate execution” in CPS-based UCHS could replace the existing “original data - human experience control execution” approach in the UCHS. The intelligibility of the CPS-based control platform is mainly reflected in the application of model prediction and optimization decision-making, that is, to cover all stages of “source-network-load-storage” while allowing for safer operation and better resource allocation in complex multi-source heating systems. The detailed comparison between traditional heating control platform and CPS-based control platform in UCHS is shown in Table 2.

12

Table 2 Comparison between CPS-Based Heating Control and Traditional Heating Control Features

Traditional Heating Control Platform

CPS-Based Control Platform

System Structure

It has multiple independent systems It adopts an integrated system design with all linking to different functions. functions integrated.

Control Objective

It focuses on the analysis and control It realizes integrated operation control in the of local units, lacking unified whole process of “source-network-chargecoordination and information sharing. storage” in the heating system.

Control Cycle

It takes “day” as the basic unit to carry It takes “minute” as the basic unit to carry out out long-term planning. short-term predictive control.

Control Mode

Operators control manually based on It provides an optimal control strategy based experience. on modeling and prediction.

Optimization Capability

It relies on experience and could not It provides optimization decision based on guarantee economic, stable, and safe thermal-hydraulic and data-driven model and operation. can realize multi-objective optimization

Visualization

It provides dynamic, three-dimensional data The data of heating system operation visualization together with the geographical are displayed statically. information.

2.3

Modeling and Model Calibration

In practice, system models are critical to the success of such a CPS-based framework. In UCHS, the thermal-hydraulic model could reflect the nature of the actual process and the internal thermal-hydraulic coupling in the heating network and sources. •

Thermal-hydraulic Modeling

In general, the dynamic models of mass and energy transfer in components of UCHS can be described by the Eq. (1), which applies to both heating sources and heating network:

∑(

Fin F dF ) − ∑ ( out ) = t t dt

(1)

where Fin represents the input of mass or energy, Fout represents the output of mass or energy, and t represents time. In order to solve the thermal-hydraulic model of UCHS with a complex topological structure, the model can be translated into a directed topological graph G(N, S) based on graph theory [28]. With the

13

application of graph theory, the thermal equipment such as heating sources, heating substations can be abstracted into a node with mass and heat flow in and out. The connecting pipe can be interpreted as a section that connects two nodes. In graph G(N, S), N represents the set of all nodes and S represents the set of all sections. In UCHS with ring-shaped network, there will be several circuits that contain all the sections in the main circuits of the directed graph with the minimum number of circuits, known as “independent circuits”, represented as C. By assuming that UCHS has I nodes, J sections and K independent circuits, the heating network follows Eq. (2):

K = J − I +1

(2)

To describe the topological structure of UCHS, a matrix U=(uij) with I rows and J columns needs to be

defined so that the connection relations between any node Ni and section Sj in graph G can be represented, as shown in Eq. (3) and Eq. (4):

 u11 ... u1 j M M M  U =  ui1 ... uij  M M M u I 1 ... uIj 

1  uij = −1 0 

... u1J  M M  ... uiJ   M M  ... uIJ 

(3)

S j connects with Ni and points towards Ni S j connects with Ni and points away from Ni

(4)

S j does not connect with Ni

Similarly, another matrix W=(wkj) is also defined to describe the relation between each independent circuit Ck and section Sj.

 w11 ... w1 j  M M M  W =  wk 1 ... wkj  M M  M  wK 1 ... wKj 

1  wkj = −1 0 

... w1J  M M  ... wkJ   M M  ... wKJ 

S j is part of Ck and in the same direction S j is part of Ck and in different directions S j is not part of Ck

The net mass flow rate of nodes can be expressed as column vector G: 14

(5)

(6)

G = [G1,..., Gi ,..., GI ]

T

(7)

If Gi is positive, Ni has net output mass flow; Otherwise, Ni has net input mass flow. The mass flow rate of sections can be expressed as column vector m:

m =  m1 ,..., m j ,..., m J 

T

(8)

The pressure loss of sections can be expressed as column vector ∆P:

∆P =  ∆ P1 ,..., ∆ Pj ,..., ∆ PJ 

T

(9)

Where ∆Pj consists of friction resistance pressure loss ∆Pfri.j, local resistance pressure loss ∆Ploc.j, dynamic pressure loss ∆Pdyn.j and gravitational pressure loss ∆Pgra.j. ∆Pfri.j and ∆Ploc.j are related to the flow state of the fluid and can be described as follows.

∆Pfri. j = λ j

8

lj

π gd

∆Ploc. j = ∑ξ j

2

5 j

m 2j

(10)

8 1 2 m π 2 g d 4j j

(11)

Where λj represents the friction resistance coefficient of section Sj which is affected by flow state and the relative roughness of pipe wall. lj represents the length of section Sj. dj represents pipe diameter of section Sj. ∑ξj represents the sum of local resistance coefficients of section Sj. g represents gravitational acceleration. In general, this study uses Ej to refer to all characteristic parameters determined by section Sj itself, which is known as flow resistance factor [29]. Thus ∆Pj can be also described as:

∆Pj = E j m2j − ∆Pdym. j + ∆Pgra. j

(12)

The net heat transfer of each node can be expressed as column vector Qnode:

Qnode = [Qnode.1,..., Qnode.i ,..., Qnode.I ]

T

(13)

where if Qnode.i is positive, Ni has net output heat; if Qnode.i is negative, Ni has net input heat. The heat loss model of the pipe section is based on the longitudinal temperature drop, which can be described as:

15

Q section =  Qsec tion.1 ,..., Qsec tion . j ,..., Qsec tion . J 

T

Qsec tion. j = m j ⋅ cTavg . j ⋅ ∆T j

(14) (15)

Where cTavg.j represents the specific heat of fluid (usually hot water) under the average temperature in section Sj. ∆Tj represents the temperature difference between both ends of section Sj. Meanwhile, the heat loss of each pipe section can be also expressed by the integral of radial heat loss per unit length:

Qsection. j = ∑k j ( x) ⋅[Tj ( x) − Tamb ]dx k j ( x) =

1 Rin. j ( x) + R p. j ( x) + Risu . j ( x) + Rout . j ( x)

(16)

(17)

Where kj(x) represents the overall heat transfer coefficient at position x of section Sj. Rin.j(x) represents the thermal resistance of heat convection between the fluid and the inner wall of the pipe section, Rp.j(x) represents the thermal resistance of heat conduction of the pipe wall, Risu.j(x) represents the thermal resistance of heat conduction of the insulation layer, Rout.j(x) represents the thermal resistance of heat convection and radiative heat transfer between the outer layer of the insulation and the air. Tj(x) represents the average temperature at position x of section Sj. Tamb represents the ambient temperature. Based on our previous researches [30], Kirchhoff’s laws can be applied to fluid flowing in pipe network. The flow rate conservation law of nodes and energy conservation law of independent circuits are respectively described in Eqs. (18) and (19):

U ⋅m = G

(18)

W ⋅ ∆P = 0

(19)

In combination with the formulas that describe the relationship between pressure loss and flow rate, and the relationship between heat loss and flow rate, the complete thermal-hydraulic model can be expressed by Eq. (20) and Eq. (21).

16

∆P = E ⋅ m ⋅ m − ∆Pdym + ∆Pgra

(20)

Qsection = m ⋅ cTavg ⋅ ∆T

(21)

Where E, |m | and cTavg are diagonal matrix whose diagonal elements are respectively Ej , absolute value of mj and cTavg.j. ∆Pdyn, ∆Pgra and ∆T are column vector whose elements are respectively ∆Pdyn.j, ∆Pgra.j and ∆Tj.

•

Model Solver

The aim of thermal-hydraulic model solver is to figure out the distribution of mass flow rate, pressure loss and temperature difference in the whole network of UCHS. Considering the coupling relation between mass flow rate and pressure loss of the pipe network shown, this study applies the method of iterative approximation to obtain m and ∆P. Based on the flow rate conservation law, the initial flow rate of each pipe section can be given as follow:

m (0) =  m1(0) ,..., m j (0) ,..., mJ (0) 

T

After the initial flow rate distribution calculation, non-zero values of W ⋅ ∆P

(22) (i )

are used to modify the

flow rate of each pipe section. Finally, the solver obtains the approximate solution of m and ∆P when the maximum of W ⋅ ∆P of all pipe sections is less than preset value εp. After completing the iteration of hydraulic calculation, the solver carries out thermal calculation by giving an initial temperature difference ∆T(0). In order to achieve accurate thermal calculation of long pipe section, the discrete unit length x is set as follows:

l l l  x =  1 ,..., j ,..., J  xj xJ   x1

T

(23)

Where x j ∈ N * , which are determined by the length and insulation state of pipe section Sj. Based on the discrete unit length x, the average temperature Tj(x)(i) can be obtained by equal division. The heat loss Qsection(i) of each pipe section can be calculated by the integral of radial heat loss per unit length defined in

Eqs. (16) and (17). The solver uses the temperature difference ∆Tlon(i) given in Eq. (21) to modify ∆T(i). Finally, the solver obtains the output of ∆T when the maximum of ∆T - ∆Tlon of all pipe sections is less than preset value εT. The overall process of thermal-hydraulic model solution is shown in Figure 3.

17

Figure 3 Overall Process of Thermal-hydraulic Model Solver

•

Online Model Calibration

One major source accounting for the difference between thermal-hydraulic simulation results and field data arises from the uncertainty of model parameters such as flow resistance factor. The major challenge of applying thermal-hydraulic modeling to a real UCHS then becomes how to yield a satisfying model accuracy by online model calibration. Online model calibration in UCHS is to search for flow resistance factors and heat transfer coefficients in thermal-hydraulic model based on the online measuring data. As for the thermal-hydraulic model, the calibration can be regarded as the inverse problem of model solution. When calibrating models of largescale systems with a large number of relevant parameters, it is a popular choice to use an optimization technique, which is also called implicit calibration method [32]. Implicit model calibration needs an objective function to minimize the difference between the optimization value and the measured value. Therefore, the calibration problem is formulated as a nonlinear objective function subject to a set of constraints. In this study, the objective function for online model calibration in UCHS is shown as follow: A

PND

a =1

i =1

(

min f ( PARA) = ∑ [ ∑ PS ia − PM ia

) ∑( 2

+

MSEC j =1

MS ja − MM ja

)

2

TND

(

)

2

+ ∑ TS ka − TM ka ]] (24) k =1

Where PARA represents the model parameters to be calibrated. A represents the total number of sets of measuring conditions for model calibration. PND, MSEC, TND respectively represent the total number of 18

measuring points of node pressure, section mass flow rate and node temperature. PS, MS and TS respectively represent model simulation result of node pressure, section mass flow rate and node temperature. PM, MM and TM respectively represent measured data of node pressure, section mass flow rate and node temperature. During the calibration, the normalized values (rather than the original values) of PM, MS, TS, PM, MM and TM are used to speed up the optimization process. Normalization was carried linearly, as shown in Eq. (25).

X=

X − X min X max − X min

(25)

Where X ∈{ PS , MS , TS , PM , MM , TM } . The inequality constraints are used to set limits on upper and lower bounds of the decision variables (parameters to be calibrated) of the calibration problem.

E min ≤ E j ≤ E max j j

(26)

k min ≤ k j ≤ k max j j

(27)

Due to cost control, the number of added measuring points in the whole network of large-scale UCHS is limited. In practice, the whole network of heating system can also be divided into several sub-networks according to the number of measuring points required in model calibration. With such an approach and calibration methodology, the developed approach has two notable improvements when compared with the approaches and tools summarized by Allegrini et al. [8]. First, such an approach could deal with both industrial heating systems (with high-pressure and high-temperature steam as the working medium) and residential heating systems (water as the working medium). Moreover, due to the separation of the topological structure and the node-wise calculation, it supports a hybrid system where both steam and water are used in different regions of the same system, as is found in a lot of Chinese industrial cities with waste heating recovery. 2.4

Real-Time Load Distribution Optimization

Operation optimization is based on on-line modeling of heating system and serves as the output of modelbased predictive control. It takes the UCHS model, operation state, internal and external constraints into account, and periodically adjusts the parameters of UCHS. It ensures that the UCHS keeps up with extrinsic factors such as emission requirements and fuel pricing while maintaining a “best” state regarding either safety, efficiency or environmental impact. It can provide on-line analysis and operation optimization of heating sources and heating network. 19

The optimal control of load distribution among multiple heating sources is a complex multi-objective nonlinear programming problem subject to large-scale, multi-dimensions, and strong-coupling constraints. In UCHS and other energy system research, a popular choice is to use particle swarm optimization algorithms [33,34]. To obtain a higher convergence speed in large-scale network, this study used a global particle swarm optimization algorithm with restart strategy. The final outcome is an optimization framework consisting of computing/database servers and optimization modules searching for real-time optimal solution, as shown in the flowchart of Figure 4.

Figure 4 Multi-Sources Load Distribution Optimization of UCHS

In Figure 4, the objective function of multi-sources load distribution optimization is set in Eq. (28) BO

CHP

i =1

j =1

min{∑ Fbo.i (Qi ) + ∑ Fchp. j (Q j ) + HS

∑E k =1

SO2 .k

HS

(28)

(Qk )δ SO2 +∑ ENOx .k (Qk )δ NOx } k =1

Where BO represents total number of gas-fired boilers. CHP represents total number of coal-based CHP units. HS represents total number of all heating sources units. Fbo.i(Qi) and Fchp.j(Qj) respectively represent the operation costs for the boiler i and CHP unit j to supply heat Q. ESO2.k(Qk) and ENOx.k(Qk) respectively represent the emissions of SO2 and NOx from the heating source unit k to supply heat Q. δSO2 and δNOx respectively represent the emission penalty price. 20

The equality constraint of the optimization model is heat balance constraint: BO

CHP

i =1

j =1

∑Qi + ∑ Qj − ∑Qsection = ∑Qnode

(29)

The inequality constraints include load limit constraints, pollutant emission constraints and pipeline transport capacity constraints:

Qimin ≤ Qi ≤ Qimax

(30)

Qmin ≤ Q j ≤ Qmax j j

(31)

HS

∑E

SO2 .k

(Qk ) ≤ LSO2

(32)

NOx .k

(Qk ) ≤ LNOx

(33)

k =1

HS

∑E k =1

Pbl − ( ∑ ∆P)bl ≥ ∆Pset

(34)

Where LSO2 and LNOx respectively represent the emission limits of SO2 and NOx. Pbl represents the lift of main pump for the branch line. (∑∆P)bl represents the total pressure loss of all sections in the branch line. ∆Pset is a set value of pressure difference which can meet the transport condition. Valves and pumps can

be set as optimization variables to meet pipeline transport capacity constraints.

3. Validation and Case Study 3.1

Case Introduction

To explain the CPS-based control platform of UCHS, a real UCHS in Sanhe City adjacent to Beijing was selected as the field test site. This study built a model of this UCHS and further developed the CPS-based control platform. Figure 5 shows the graphic layer of the model. The load distribution of multiple heating sources in December 2017 is also shown in Figure 5. The heating source of this UCHS is made up of coal-based CHP units and gas-fired boilers, as shown in Figure 6. The UCHS has four coal-based CHP units: unit #1 and #2 were designed to meet a total heating load of 500 MW; unit #3 and #4 were designed to provide another heating load of 620 MW. In addition to CHP units, it has four gas-fired hot water boilers each with a heating capacity of 116 MW. There are 230 heating substations in the network. The

21

overall heating area is supported by two main pipelines: line A (blue line) and line B (green line). The heating area covered by line A is 8.5 million m2, mainly supplied by unit #1 and #2. The heating area covered by line B is 7.0 million m2, mainly supplied by unit # 3 and # 4.

Figure 5 UCHS in Sanhe City

Fig. 6a) Coal-Based CHP Units

Fig. 6b) Gas-Fired Hot Water Boilers

Figure 6 Heating Sources 3.2

Setup of CPS-Based Control platform

This study built the CPS-based control platform based on the technical roadmap shown in Figure 1and Figure 2. It needs to be pointed out that the control platform was based upon the existing heating metering 22

system. In the meanwhile, the heating control platform also utilized the existing automation system in heating sources and heating substations. To provide more sensing capability to the system, wireless measuring devices and electric regulating valves were installed in the midst of heating network. The realtime data from sensors were connected with existing SCADA and distributed control system (DCS) systems to form a unified data flow. Computing and database servers were further built and integrated to support the real-time information exchange between UCHS and the platform. Once the database server accessed the unified data flow, the computing server called the historical data and online data of the database to generate the results of load forecasting. It then carried out a model-based real-time analysis before reaching a decision based on online optimization target. What needs to be pointed out is that achieving the hydraulic and thermal balance of the network could last for a couple of hours. However, actuators such as valves and pumps could operate much faster. Therefore, it is possible and in fact necessary to carry out prediction-based control for such UCHS. The thermal-hydraulic model and its corresponding calibration were carried out based on the methodology shown in section 2.3. The optimization decision-making module was built based on the methodology shown in section 2.4. 3.3

Model Validation

As part of the calibration and validation process, this study focused on the flow resistance factors in the network through the collection of the operation data in key positions such as remote nodes, junction nodes, and nodes near high-heating-demand communities. During the field testing, this study used 21 pressure measuring devices installed in the midst of heating network. The whole network was then divided into 11 sub-networks for online model calibration, as is shown in Figure 7.

23

Figure 7 Measuring Points Layout and Heating Sub-networks Division for Model Calibration

In order to verify the accuracy of the thermal-hydraulic model, the measured and calculated water supply and return pressure data of 184 heating substations were analyzed. The results of data comparison are shown in Figure 8 and Figure 9.

Figure 8 Comparison of Measured and Calculated Data of Water Supply Pressure

24

Figure 9 Comparison of Measured and Calculated Data of Water Return Pressure

Among them, the proportions of heating substations with relative error of water supply and return pressure less than 5% are 88.5% and 82.1%, which means the simulation results of thermal-hydraulic model meet the accuracy requirements. Detailed statistical results are shown in Table 3.

25

Table 3 Statistical Results of Comparison between Measured and Calculated Data of Water Supply and Return Pressure Error Range

Number

Percentage (%)

Relative error of water supply pressure is less than 2%

77

41.8

Relative error of water supply pressure is between 2% and 5%

86

46.7

15

8.2

3

1.6

Relative error of water supply pressure is more than 20%

3

1.6

Relative error of water return pressure is less than 2%

103

56.0

Relative error of water return pressure is between 2% and 5%

48

26.1

11

6.0

16

8.7

6

3.3

Relative error of water supply pressure is between 5% and 10% Relative error of water supply pressure is between 10% and 20%

Relative error of water return pressure is between 5% and 10% Relative error of water return pressure is between 10% and 20% Relative error of water return pressure is more than 20%

3.4

Multi-Sources Load Distribution Optimization

The multi-sources load distribution optimization of UCHS can utilize the economic and environmental advantages of different heating sources and also enhance the heat supply quality. Pilot operation was carried out during the heating season of 2017. The load distribution of multiple heating sources from 12/10/2017 to 12/12/2017 with conventional experience-based control (optimization feature disabled) is shown in Figure 10. At the beginning of the two-day operation, the area covered by line A was heated by unit #1 and #2. The area covered by line B was heated by the boilers. It should be noted that in this case the gas-fired boilers were used as the main heating source, leading to high consumption of natural gas. During the operation, the operation cost was high due to the pricing of natural gas. Meanwhile, the heating output of unit #1 and #2 was close to 300 MW combined, leaving space for load distribution when compared with the maximum heating output. On-line load distribution optimization was carried out

26

to maximize the output of unit #1 and #2 and reduce the load of the boilers to ensure more economical operation. The result was the new operation scheme of the heating network shown in Figure 11.

Figure 10 Real-Time Operation with Optimization Feature Disabled: Load Distribution of Multiple Heating Sources

27

Figure 11 Real-Time Operation with Optimization Feature Activated: Load Distribution of Multiple Heating Sources

The optimized scheme turned off the shutoff valve (as shown in Figure 11) and adjusted the combination of pumps in order to satisfy the transport capacity constraints. Under such a scheme, the boilers were redirected to supply line A, as can be found in Figure 11. After adopting this new operation scheme, unit#1 and #2 acted as main heating sources and provided a total hot water flow rate of 6905 t/h. Gasfired boilers were used as peaking heating sources and could provide 1500 t/h of hot water if needed. It led to a full-capacity operation of unit #1 and #2 with reduced consumption of natural gas. The typical heat load distributions before and after optimization are shown in Table 4. Table 4 Heat Load of Heating Sources with and without Optimization Feature Two-Day Operation: Heating Sources

Two-Day Operation:

Heat Load of Heating Sources Heat Load of Heating Sources with without Optimization Feature

Optimization Feature

(MW)

(MW)

Gas-fired boilers

240

103

Unit #1 and #2

310

494

Unit #3 and #4

110

121

28

During the two-day operation, it is found that CPS-based dynamic balance control could generate optimized scheme reducing natural gas consumption by up to 31.2% and reducing the total heating cost by 2.6% when compared with conventional experience-based control approach. Even with the total heat load increased by 8.8%, the total cost including operation cost and emission penalty cost in optimized scheme is basically equal to that in existing scheme (as shown in Figure 12), which shows economic advantages. That is because the existing heating control uses fixed operation scenario change and has to cause overheating to ensure heating load balance under various operation conditions, therefore causing a waste of primary energy.

Figure 12 Total Cost of Heating Sources Before and After Optimization 4. Conclusions

This study reviews the concept of CPS and discusses how it could be borrowed into the area of energy system (especially large-scale UCHS). It proposes a new UCHS operation control platform, including its technology roadmap and CPS-based framework. Based on the roadmap and framework, this study further develops a novel control platform. In order to yield a satisfying model accuracy for such CPS-based control platform during the seasonal operation, an implicit model calibration method is developed to search the real-time operation parameters by using field measured data. After that, the platform was implemented in a UCHS adjacent to Beijing to demonstrate the its effectiveness and feasibility. It is found that the developed CPS-based control platform shows sufficient accuracy to be used in pilot test. 88.5% of the modeled substation supply pressure has an error less than 5% and 82.1% of the modeled return pressure has an error less than 5%. With the validated platform, this study investigates the impact of the multi-sources load distribution optimization feature of the developed platform in the real UCHS. During a two-day pilot operation in December 2017, the results show that CPS-based operation control could

29

reduce natural gas consumption by up to 31.2% and reduce the total heating cost by 2.6% when compared with conventional experience-based control. 5. Future Work

The ultimate goal of the CPS-based heating control platform is to endow the heating system with human’s wisdom. One highlight of experienced human operators is that they could selectively obtain data based on the experience of dealing with both structured standard data such as temperature, pressure and flow rate, and unstructured/stochastic data such as thermal comfort. However, the developed platform in this study is limited in terms of data selectivity and processing. Although the CPS-based platform is powerful in processing the data collected in the field, it could not effectively handle low-quality and unstructured data. Moreover, another aspect of research included in this paper is the introduction of real-time loaddistribution optimization in large-scale UCHS. Although PSO algorithm is successfully used and tested in the pilot operation of the demo site, it is still necessary to the compare the effectiveness of different operation algorithms (such as a genetic algorithm) when applied to the pilot site. Therefore, one future work will be the field performance of the developed system with low-quality data and different algorithms. Although the approach developed in this paper could support online operation optimization of multisource large-scale complicated heating system covering a heating area as high as 100 million m2, the validity and effectiveness of such approach under such mega-scale heating system still need field data justification. This is one limitation of our approach. For such a mega-scale system, the amount of data involved during the online computation and operation mode switch needs to be partitioned, which is a critical part of the future work. In addition to that, other future work will be how to construct a unique mode used in real-time analysis by introducing the human perspective into the UCHS model to allow for the sense of “fuzzy” modeling, that is, the human operators could have the discretion of the complexity of the UCHS model to reduce the time of model creation. Meanwhile, in the process of optimization decision-making, it is also worthwhile developing a new decision-making mechanism where the results from CPS-based platform are incorporated with the subjective decision of human beings, which could possibly lead to better coordination of all aspects of the UCHS. 6. Acknowledgments

This work is supported by National Key R & D Program of China (Grant No. 2017YFA0700305). This work is in part supported by National Natural Science Foundation of China (Grant No. 51806190). This work is also supported by key project of Beijing Municipal Science and Technology Commission “Blue 30

Sky Project” (Grant No. D171100001217001). This work is also in part supported by Natural Science Foundation of Zhejiang Province (Grant No. LY17F030007). References

[1]

Tsinghua University, IEA. District Energy Systems in China. 2018.

[2]

Fang H, Xia J, Lin B, Jiang Y. Study on the Current Situation and Technical Route of Clean

Heating in Northern Cities. Dist Heat 2018:11–8. [3]

Zhang J, Sun J, Miao Y. Cause Analysis and Countermeasures of Fog and Haze Weather. China

Emerg Manag 2000;18:109–19. [4]

Wang H, Yin W, Abdollahi E, Lahdelma R, Jiao W. Modelling and optimization of CHP based

district heating system with renewable energy production and energy storage. Appl Energy 2015;159:401–21. doi:10.1016/j.apenergy.2015.09.020. [5]

Dimoulkas I, Amelin M, Levihn F. District heating system operation in power systems with high

share of wind power. J Mod Power Syst Clean Energy 2017;5:850–62. doi:10.1007/s40565-017-0344-6. [6] system

Gabrielaitiene I, Bøhm B, Sunden B. Modelling temperature dynamics of a district heating in

Naestved,

Denmark-A

case

study.

Energy

Convers

Manag

2007;48:78–86.

doi:10.1016/j.enconman.2006.05.011. [7]

Mattias V, Dahl J. A method for the simulation and optimization of district heating systems with

meshed networks. Energy Convers Manag 2015;89:555–67. doi:10.1016/j.enconman.2014.10.002. [8]

Allegrini J, Orehounig K, Mavromatidis G, Ruesch F, Dorer V, Evins R. A review of modelling

approaches and tools for the simulation of district-scale energy systems. Renew Sustain Energy Rev 2015;52:1391–404. doi:10.1016/j.rser.2015.07.123. [9]

Lund H, Werner S, Wiltshire R, Svendsen S, Thorsen JE, Hvelplund F, et al. 4th Generation

District Heating (4GDH). Integrating Smart Thermal Grids into Future Sustainable Energy Systems. Energy 2014;68:1–11. doi:10.1016/j.energy.2014.02.089. [10]

Østergaard PA, Lund H. A renewable energy system in Frederikshavn using low-temperature

geothermal

energy

for

district

heating.

Appl

Energy

2011;88:479–87.

doi:10.1016/j.apenergy.2010.03.018. [11]

Utlu Z, Aydin D, Kincay O. Comprehensive thermodynamic analysis of a renewable energy

sourced hybrid heating system combined with latent heat storage. Energy Convers Manag 2014;84:311– 25. doi:10.1016/j.enconman.2014.04.024. 31

[12]

He J, Chen L, Xu F, Min Y. Optimal planning of clean heating with renewable energy sources.

Proc - 1st IEEE Int Conf Energy Internet, ICEI 2017 2017:1–6. doi:10.1109/ICEI.2017.8. [13]

Vigants G, Blumberga D. Modelling of the district heating system’s operation. Environ Clim

Technol 2011;6:132–7. doi:10.2478/v10145-011-0019-x. [14]

Wanjiru EM, Sichilalu SM, Xia X. Model predictive control of heat pump water heater-

instantaneous shower powered with integrated renewable-grid energy systems. Appl Energy 2017;204:1333–46. doi:10.1016/j.apenergy.2017.05.033. [15]

Nikula H, Vesaoja E, Sierla S, Karhela T, Flikkema PG, Aikala A, et al. Co-simulation of a

dynamic process simulator and an event-based control system: Case district heating system. 19th IEEE Int Conf Emerg Technol Fact Autom ETFA 2014 2014. doi:10.1109/ETFA.2014.7005184. [16]

Åberg M, Widén J, Henning D. Sensitivity of district heating system operation to heat demand

reductions

and

electricity

price

variations:

A

Swedish

example.

Energy

2012;41:525–40.

doi:10.1016/j.energy.2012.02.034. [17]

Nielsen HA, Madsen H. Modelling the Heat Consumption in District Heating Systems Using a

Grey-box Approach. Energy Build 2006;38:63–71. doi:10.1016/j.enbuild.2005.05.002. [18]

Ferraty F, Goia A, Salinelli E. Peak-Load Forecasting Using a Functional Semi-Parametric

Approach. Top Nonparametric Stat Springer New York 2014;74:367. [19]

Ganchev I, Ji Z, O’Droma M. An IoT-based smart electric heating control system: Design and

implementation.

Int

Conf

Ubiquitous

Futur

Networks,

ICUFN

2017:760–2.

doi:10.1109/ICUFN.2017.7993895. [20]

Tunzi M, Boukhanouf R, Li H, Svendsen S, Ianakiev A. Improving thermal performance of an

existing UK district heat network : A case for temperature optimization. Energy Build 2018;158:1576–85. doi:10.1016/j.enbuild.2017.11.049. [21]

Yu X, Xue Y. Smart Grids : A Cyber – Physical Systems Perspective. Proc IEEE 2016;104:1058–

70. [22]

Rajkumar R, Lee ILI, Sha LSL, Stankovic J. Cyber-physical systems: The next computing

revolution. Des Autom Conf (DAC), 2010 47th ACM/IEEE 2010:0–5. doi:10.1145/1837274.1837461. [23]

Faruque MA Al, Ahourai F. A model-based design of Cyber-Physical Energy Systems. Proc Asia

South Pacific Des Autom Conf ASP-DAC 2014:97–104. doi:10.1109/ASPDAC.2014.6742873.

32

[24]

Ramos C, Vale Z, Faria L. Cyber-physical intelligence in the context of power systems. Futur.

Gener. Inf. Technol.

Lect. Notes Comput. Sci., vol. 7105 LNCS, 2011, p. 19–29. doi:10.1007/978-3-

642-27142-7_3. [25]

Liu D, Sheng WX, Wang Y, Lu YM, Sun C. Key technologies and trends of cyber physical

system for power grid. Proc CSEE 2015;35:3522–31. doi:10.13334/j.0258-8013.pcsee.2015.14.006. [26] IEEE

Chen KT, Luo TT. Sensing-driven Energy purchasing in Smart Grid Cyber-physical System. Trans

Syst

Man,

Cybern

Part

ASystems

Humans

2013;43:773–84.

doi:10.1109/TSMCA.2012.2224337. [27]

Maasoumy M. Controlling Energy-Efficient Buildings in the Context of Smart Grid: A Cyber

Physical System Approach. Health Policy (New York) 2014. [28]

JA B, USR M. Graph Theory with Applications. The Macmillan Press Ltd; 1976.

[29]

Lopes AMG. Implementation of the hardy-cross method for the solution of piping networks.

Comput Appl Eng Educ 2004;12:117–25. doi:10.1002/cae.20006. [30] systems

Yan A, Zhao J, An Q, Zhao Y, Li H, Huang YJ. Hydraulic performance of a new district heating with

distributed

variable

speed

pumps.

Appl

Energy

2013;112:876–85.

doi:10.1016/j.apenergy.2013.06.031. [31]

Zhong W, Feng H, Wang X, Wu D, Xue M, Wang J. Online hydraulic calculation and operation

optimization of industrial steam heating networks considering heat dissipation in pipes. Energy 2015;87:566–77. doi:10.1016/J.ENERGY.2015.05.024. [32] systems

Wang J, Zhou Z, Zhao J. A method for the steady-state thermal simulation of district heating and

model

parameters

calibration.

Energy

Convers

Manag

2016;120:294–305.

doi:10.1016/j.enconman.2016.04.074. [33]

Yang P, Ai X, Cui M, Lei Z. Particle Swarm Optimization Based Economic Operation Analysis

of Microgrid Containing Multi Energy Supply System. Power Syst Technol 2009:38–42. doi:10.13335/j.1000-3673.pst.2009.20.023. [34]

Wang J, Zhai ZJ, Jing Y, Zhang C. Particle swarm optimization for redundant building cooling

heating and power system. Appl Energy 2010;87:3668–79. doi:10.1016/j.apenergy.2010.06.021.

33

Highlights •

“Cyber-physical systems” idea is applied to urban centralized heating system.

•

A CPS-based control platform is developed and further tested in a real site.

•

The platform reduces overall energy consumption during pilot season testing.

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: