Optimal integrated demand response scheduling in regional integrated energy system with concentrating solar power

Applied Thermal Engineering 166 (2020) 114754

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Optimal integrated demand response scheduling in regional integrated energy system with concentrating solar power

T

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Peng Jiang , Jun Dong, Hui Huang School of Economics and Management, North China Electric Power University, Changping District, Beijing 102206, China

H I GH L IG H T S

basic structure of regional integrated energy system including CSP system is proposed. • The model and strategy of RIES-CSP based on Integrated Demand Response are established. • Optimization carbon trading mechanism is considered in operational optimization. • Stepped • Objective function takes into account the Integrated Demand Response benefits of electricity and thermal.

A R T I C LE I N FO

A B S T R A C T

Keywords: Regional integrated energy system Concentrating solar power Operation optimization Integrated demand response Ladder carbon emission Operational strategy

The Regional Integrated Energy System Concentrating Solar Power (RIES-CSP) can realize the energy conversion between solar energy, thermal energy and electric energy, which provides an effective way to solve the problem of solar power generation. In this paper, a concentrating solar power station is introduced in the integrated energy system of combined thermal and power supply to assist the system operation, and a typical system structure architecture is constructed by combining energy conversion equipment such as wind power station, energy storage device and electric heater. Moreover, the electricity price and the thermal price are used as signals to guide the system to participate in the Integrated Demand Response program (IDR), and the Demand Response model of electric load and thermal load is established based on the price elasticity matrix. On this basis, considering the uncertainty of renewable energy, ladder carbon emission and the DR benefit, the system operation optimization model is established with the goal of minimizing the operation cost of integrated energy system. The model presented could reduce the cost of system without causing a significant amount of environmental pollutions, and improve the energy efficiency of the system operation efficiently. Finally, a RIESCSP was used for simulation analysis. The simulation results show that the system participates in the IDR program and exchanges energy with the energy market, which makes the DR effect more obvious, and the total operation cost is 7.85% lower than the traditional operation mode.

1. Introduction Intermittent renewable energy power generation technologies, such as the absorption of wind power, and solar energy and multi-energy conversion technology, have gradually become research hotspots in the field of power systems. The solar energy resources directly radiated in China are mainly concentrated in the west and the north. At the same time, these places also have rich wind energy resources and strong thermal load demand [1–2]. Wind power and photovoltaic power output are intermittent and uncertain, which will affect the safety and economy of power system operation. The regional integrated energy system solves the problem of absorption of distributed wind power and

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photovoltaic power to a certain extent [3]. The regional integrated energy system can organically integrate various distributed renewable energy sources, and can realize mutual conversion of various energy sources internally. The conversion between different energy sources and the interaction between supply and demand sides in the regional integrated energy system can effectively alleviate the negative impact of the uncertainty of renewable energy output [4]. With the continuous strengthening and deepening of the connection between the power system and the heating system, there have been some researches on the optimal operation of the regional integrated energy system. In terms of system source-side operation optimization, literature [5] provided reference for micro-grid power selection by

Corresponding author. E-mail address: [email protected] (P. Jiang).

https://doi.org/10.1016/j.applthermaleng.2019.114754 Received 18 June 2019; Received in revised form 10 November 2019; Accepted 30 November 2019 Available online 02 December 2019 1359-4311/ © 2019 Elsevier Ltd. All rights reserved.

Applied Thermal Engineering 166 (2020) 114754

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Nomenclature

yuan the standard discrimination of the ladder discharge penalty, kg Pi (t ) the power of the emission source i at time t , kW The unit price of the pollutant m , yuan / kg θm the power that the system input energy to the urban enPEsell ( t ) _grid, i ergy network, kW γE − dr , i the energy price of IDR, yuan /kWh I the IDR coefficients Δγe − dr , i the change in retail electricity price after the IDR is implemented, yuan /kWh R iiE − e the “Electric load-electric price” demand response elastic coefficient Qt − Load − i the thermal load before the system participates in the IDR, kWh γt − dr , i the initial retail thermal price before the IDR is implemented, yuan /kWh ∂E−t the capacity ratio of elastic thermal load C PTES (t ) the thermal storage power of TES, kW PS − E (t ) the thermal power directly used by the thermal collecting device to generate electricity, kW PCSP − E (t ) the thermal power required for start-up of the CSP plant, kW D − load PTES (t ) the TES supplies the thermal release power of the thermal load, kW D − csp PTES (t ) the thermal release device supplies the thermal release power generated by the CSP system, kW PSEB the thermal power delivered by the electric boiler to the − T (t ) TES, kW min PCSP (t ) the minimum electric power of CSP, kW max PCSP (t ) the maximum electric power of CSP, kW up PCSP the maximum upward climbing rate of steam turbine − tg generators for CSP, kW down the maximum downward climbing rate of steam turbine PCSP − tg generators for CSP, kW Ploss (t ) the power loss, kW PE − load (t ) the electrical load power, kW C PTES (t ) the charge power of EES, kW D PTES (t ) the discharge power of EES, kW PWT (t ) the output power of the Wind Turbines, kW PCCHP − E (t ) the electrical output power of CCHP, kW PT − load (t ) the thermal load power, kW PCCHP − T (t ) the thermal output power of CCHP, kW PEB (t ) the output power of EB, kW PTES, C (t ) the charge power of TES, kW PTES, D (t ) the discharge power of TES, kW PT − grid (t ) the thermal exchange power between RIES and urban thermal network, kW PE − G (t ) the power of gas storage system, kW PG − eles (t ) the NG load power of resident, kW the utilization efficiency of NG in the CCHP system, % ηcchp SOC (t + 1) the state of capacity of EES at time t + 1 SOC (t ) the state of capacity of EES at time t ηEES − C the ES charge efficiencies ηEES − C the ES discharge efficiencies T (t ) the temperature in the room, °C Tmax (t) the upper limit of user's temperature demand for temperature control equipment, °C Tmin (t) the lower limit of user's temperature demand for temperature control equipment, °C Xi (T ) the operational state of the transferable load Ton (x i ) the time the device starts running ΔT (x i ) the device scheduling period GS1,GS2

Indices Meaning and unit RIES-CSP Regional Integrated Energy System Concentrating Solar Power IDR Integrated Demand Response program CSP concentrating solar power EES electric energy storage THS thermal energy storage GA-ERS The Standard Genetic Algorithm with elite retention strategy E-Load electrical load T-Load thermal load P0 the forecasted output value of the wind power and the CSP system, kW η1 the photo-thermal conversion efficiency C PTES (t ) the thermal storage power of TES, kW PS − E (t ) the thermal power directly used by the thermal collecting device to generate electricity, kW ηc the loss rate when charging QTES (t ) the thermal storage in the TES, kW the storage thermal and thermal release time interval, h Δt ηd the thermoelectric conversion efficiency COperation the system operation and maintenance cost, yuan CRevenue the system operating income, yuan the operation cost of the energy equipment, yuan /kW Ci CO − fu the operation cost of NG supply module, yuan γE − dr , i the energy price of IDR, yuan /kWh CO − th the cost of thermal mitigation for systems and thermal company, yuan the low calorific value of Natural Gas, kWh/m3 LHVNG CEmission the environmental cost ϖi, m the emission of the pollutanti from the emission source m per unit of electricity produced, kg/kWh GS the upper limit of basic emission standards, kg e−t CRIES − Grid the system energy sales revenue, yuan e−t CDR IDR program revenue, yuan γE , i the fixed energy price, yuan /kWh Qe − Load − i the load before the system participates in the IDR, kWh γe − dr , i the initial retail price before the IDR is implemented, yuan /kWh ∂E−e the capacity ratio of elastic electric load Δγt − dr , i the change in retail thermal price after the IDR is implemented, yuan /kWh R iiE − t the “Thermal load-thermal price” demand response elastic coefficient c PCSP (t ) the thermal power of collector, kW D PTES (t ) the thermal release power of TES, kW c PCSP (t ) the thermal power of collector, kW Dt the direct radiation index of solar illumination (DNI) D PTES (t ) the thermal release power of TES, kW PS − T (t ) the thermal power delivered by the thermal collecting device to the TES, kW ηd the loss rate when exothermic the thermal dissipation coefficient ηs PCSP (t ) the electric power of CSP, kW C the total operating cost of the RIES-CSP, yuan CEmission the system the environmental cost, yuan CO − eq the operation cost of power supply module, yuan output of the different energy equipment, kW Pi ∂fuel the NG price, yuan /m3 γE , i the fixed energy price, yuan /kWh CO − el the power exchange cost between system and power grid,

2

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is a lack of fine modeling on multi-type energy demand. Therefore, a relatively complete modeling of IDR resources and a clear control strategy for IDR resources to participate in system operation are the key points to study the collaborative optimization of IDR resources to participate in the system. Concentrating solar power (CSP) is a new type of solar power different from photovoltaic cells, which can convert direct solar energy into thermal energy through a light condensing and thermal collecting device to provide steam drive for a steam turbine and drive a generator to generate electricity [25]. CSP system is generally equipped with large-scale thermal storage devices, which can make up for the defects of short output time and large fluctuation of photovoltaic cells and output relatively stable and controllable electricity. The internal “solarthermal-electricity” multi-energy conversion mode is also conducive to the comprehensive utilization of energy in the regional integrated energy system, which can realize the bidirectional closed-loop flow of electricity-thermal energy [26–27]. In recent years, the research on CSP system has mainly focused on the cooperative optimization with other renewable energy sources. Literature [28] proposed a joint dispatching scheme of the CSP system and wind power. The electric heating device is used to supplement additional thermal energy to the CSP system, which can improve the power generation output of the CSP system and the flexibility of system operation. Literature [29] comprehensively considered the complementary characteristics of CSP system and wind power output, and constructed an optimization model taking into account the uncertainty of system operation under the framework of robust optimization. Literature [30] introduced a large-scale energy storage device into a micro-grid containing a CSP system, and analyzed the feasibility of independent operation of the micro-grid system. Literature [31] provided a new idea for combined cooling and power (CHP) by using the thermal recovery device to recover the waste heat generated by the CSP system to supply thermal load. These studies have considered the stable and reliable power output characteristics of the CSP system, but ignored the demand of thermal load. Therefore, the role and benefits of the CSP system in the regional integrated energy system have not been fully developed. The CSP system plays an important role in modern energy systems as an important form of solar energy utilization. In the traditional CSP system, the solar collecting device in the system converts the absorbed solar energy into thermal energy to supply power and thermal demand. However, with the continuous improvement of energy quality and the continuous development of energy technology, traditional CSP systems are beginning to interconnect and couple with other energy units, the Regional Integrated Energy System concentrating solar power (RIESCSP) is one of the typical interconnected energy systems [32,33]. In the RIES-CSP system, renewable energy, traditional power supply units, gas power generation and other energy equipment complement each other, and actively participate in energy market energy transactions to improve the energy efficiency of the energy system while meeting the system energy requirements. At the same time, with the diversification of energy demand and the acceleration of energy market reforms, the interaction between regional energy systems and energy markets has gradually strengthened. In the background, under the premise of considering the IDR program, the realization of the comprehensive operation benefit of the RIES-CSP system becomes the key to the efficient operation of this complex energy system. In order to provide new ideas for solving this problem, this paper studies the optimal operation problem of the RIESCSP system based on Demand Response. Firstly, the regional integrated energy system architecture including CSP system, electric boiler, wind power, electric energy storage system, electric load and thermal load is proposed, and the structure, characteristics and transformation mechanism of RIES-CSP are analyzed respectively. Secondly, the optimized scheduling model of the RIES-CSP is constructed by considering factors such as system operation cost, environmental cost and operational efficiency. Finally, an optimized simulation of a regional integrated

establishing an optimization simulation model, and improved the operation efficiency and reliability of combined cooling heating and power (CCHP). Literature [6] discussed the combination of thermal storage technology and photovoltaic generation system, and pointed out that this technology had great potential in improving energy utilization efficiency and reducing carbon emissions. Literature [7] proposed a day-ahead dispatching method for power units and an optimization model for micro-grid joint dispatching taking into account the randomness of wind power output. Considering the uncertainty of high permeability renewable energy, the optimal operation scheduling model of the system was established in heating period and air conditioning period respectively in the literature [8]. Literature [9] established a multi-objective power system optimization operation model considering wind power integration, which can reduce the randomness and instability brought by wind power integration to system operation. Literature [10] used interval to describe the uncertainty of photovoltaic output and load, and established a day-ahead economic constraint optimization model for regional integrated energy system. In terms of system transmission-side operation optimization, Literature [11] utilized a sparse semi-definite programming (SDP) relaxation to procure the optimal solution for the coordinated operation of electricity and natural gas networks. Literature [12] combined the characteristics of power network, hydraulic network and thermal network to model the power-hydraulic-thermal power flow problem. Literature [13] proposed an energy flow calculation method for power-natural gas coupled micro-energy network based on the coupling nodes of power network and Natural Gas network. In the aspect of system load-side operation optimization, literature [14] established a new system energy balance model to deal with source-load bilateral randomness, which could solve the stability problem of large-scale renewable energy access to the power system. Literature [15] considered the uncertainty of user decision, embedded the Demand Response module into the energy hub as a unit, and established a multi-energy collaborative demand response model based on random model. With the rapid development of energy internet and integrated energy system, the decentralized energy market and energy network structure make the traditional Demand Response (DR) gradually develop to the direction of Integrated Demand Response (IDR) [16]. In the aspect of traditional power Demand Response, literature [17] introduced Demand Response resources into the model as Virtual Power units, and analyzes the impact of DR resources on system economy under the power market environment. In the literature [18], an optimal operation model of the intelligent energy management system was presented with the energy-purchasing cost and energy-selling income of the industrial load concerned taken into account. Literature [19] took the minimization of system cost and pollution emission as objective functions, establishes an optimal scheduling model of demand-side resources, solves it with particle swarm optimization algorithm, and gave the corresponding optimal scheduling strategy. In the aspect of Integrated Demand Response, relevant research mainly focused on the analysis of the impact of energy market price on the behavior of various market players under the condition of multi-energy market. Literature [20] through multi-agent benefit modeling, constructed a three-party game model covering power selling agents, gas selling agents and users, and analyzed the behavior strategies of each participating agent through the Nash equilibrium results of the model. At the system level, the relevant research mainly focused on the analysis of the impact of IDR resources on the overall operating cost of the system or the comprehensive energy utilization efficiency, and mostly models and analyzes the operation control strategy of multi-energy network with the objective of minimizing the operating cost of the system [21]. However, few factors are considered in the IDR resource modeling process, and most of them are still from the perspective of power system, without considering the impact of IDR implementation on the operation of other energy systems [22–24]. At present, the modeling research on energy consumption behavior for IDR is generally relatively simple, and there 3

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structure, renewable power generation resources are important power supply units in the system. In the RIES, the effective utilization of renewable power generation resources can improve the energy efficiency of the system and reduce carbon emissions. However, in the process of utilizing renewable power resources, its grid-connected power generation problem is a key issue to achieve its efficient utilization. The essence of the economic dispatching problem of the renewable energy grid-connected system is to rationally arrange the output of the conventional unit under the premise of satisfying the conventional constraints of the system, so that the total energy consumption cost of the conventional unit is the smallest and the energy consumption rate is the largest [34]. On this basis, the optimal economic and operational benefits of the system can be achieved optimally, while achieving energy conservation and emission reduction targets. However, the renewable energy output has the characteristics of randomness, volatility and unpredictability, which makes its output prediction have greater uncertainty. In this paper, the fuzzy chance constrained programming method is used to describe the uncertainty of wind power and photovoltaic forecast output through the membership function of trapezoidal fuzzy variables [35,36].

energy system with CSP system in the north of China was carried out. Compared with the existing similar works, the main contributions of this paper are as follows: (1). A diversified structure of regional integrated energy system including CSP system is proposed, and a novel operation optimization model for the electric-thermal coupled complex energy system is presented so as to improving the comprehensive efficiency of system operation. (2). In the construction of the objective function, the influence of electrical load and thermal load on the operating state and economy of the system after participating in the Integrated Demand Response (IDR) are considered, and the response mechanism of the integrated demand response in the RIES-CSP system is quantified. (3). The validity and accuracy of the established model are verified by a typical RIES-CSP system. By simulating the dynamic IDR process between the electricity market and the thermal market, the interactive characteristics of electricity and thermal are quantitatively analyzed, and the optimal operation strategy of the system is formulated.

ϑ−P ⎧ P − P1 P1 ⩽ ϑ ⩽ P2 ⎪ 2 1 ⎪ 1 P2 ⩽ ϑ ⩽ P3 f (ϑ) = ⎨ ϑ − P4 P3 ⩽ ϑ ⩽ P4 ⎪ P3 − P4 ⎪ 0 others ⎩

2. Characteristics and mechanism of the RIES-CSP The structure of the Regional Integrated Energy System concentrating solar power (RIES-CSP) is shown in Fig. 1, which includes wind energy, solar energy, electrical energy and thermal energy. The electrical energy in RIES is mainly provided by photo-thermal power stations (PV), wind power stations (WT), gas turbine (GT) and electric energy storage devices (EES), and can also be purchased and sold in the wholesale electricity market. The thermal energy in the RIES is provided by thermal energy storage (TES), electric boilers (EB) and photothermal power stations. The load in the integrated energy system includes electrical load and thermal load, and the two loads are respectively divided into inelastic load and elastic load that can respond to retail energy price. According to the typical Regional Integrated Energy System

(1)

where f (ϑ) is the membership function of trapezoidal fuzzy variables; ϑ is the fuzzy variable; P1, P2 , P3 , P4 are fuzzy function parameters, and P1 < P2 ⩽ P3 < P4 , when P2 = P3 , trapezoidal fuzzy variable becomes triangular fuzzy variable. Assuming that the predicted value of wind power and CSP system is P0 , the mathematical models of wind power and photovoltaic output under different fuzziness are as follows:

Fig. 1. The typical structure of Regional Integrated Energy System Concentrating Solar Power. 4

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⎧ P1 = ⎪ P2 = ⎨ P3 = ⎪ P4 = ⎩

ϖ1 P0 ϖ2 P0 , ϖ ∈ (0, 1) ϖ3 P0 i ϖ4 P0

power generation of the CSP system is as follows [41]. C

⎧ PCSP (t ) = η ⎡P c (t ) − PTES (t ) + P D (t ) η ⎤ CSP TES d d ηc ⎣ ⎦ ⎨ C D P ( t ) P ( t ) = 0 TES TES ⎩

(2)

where P0 is the forecasted output value of the WT and the CSP system, kW; P1, P2, P3, P4 indicate the wind power and photovoltaic power generation output values with different confidence levels; ϖi represents different confidence levels. CSP system is composed of three parts: light field, thermal storage system and thermal cycle. The energy transfer between the three parts is carried out by heat transfer fluid. CSP system uses thermal collector to absorb solar energy and convert it into thermal energy. It transfers thermal energy to thermal cycle system through thermal transfer fluid, and then generates steam to drive steam turbine to get electric energy, which realizes the conversion process of “solar-thermal-electricity” [37]. At the same time, thermal energy can be stored in TES system through thermal transfer fluid, and generate electricity according to dispatching demand. CSP system is obviously different from other new energy power generation, mainly embodied in the configuration of TES system, which can control the conversion process of “solar-thermalelectricity”, and then regulate the output of power generation [38]. The CSP power station uses thermal collector to absorb solar energy and convert it into thermal energy. It transfers thermal energy to thermal cycle system through thermal transfer fluid, and then generates steam to drive steam turbine to get electric energy, which realizes the conversion process of Photo-thermal and thermal-electric power. At the same time, thermal energy can be stored in thermal storage system through thermal transfer fluid, and generate electricity according to dispatching demand. Fig. 2 shows the basic principles of CSP system operation. And the mechanism of thermal energy collection, conversion and loss in CSP power plant system is described in detail below [39,40]. CSP power station converts the light energy reflected by the mirror field into thermal energy through a thermal collecting device, and the obtained thermal power is as follows: c PCSP (t )

= η1 SDt

(6)

where PCSP (t ) is the electric power of CSP, kW; ηd is the thermoelectric C D (t ) PTES (t ) = 0 indicates that the thermal conversion efficiency; PTES storage and thermal release processes cannot be performed simultaneously. 3. Optimized scheduling model of the RIES-CSP 3.1. Optimization objective In the operation optimization model established in this paper, the RIES-CSP system operation optimization problem is aimed at the optimal economic scheduling of the system. The specific optimization process always takes the minimum total operating cost of the system as the objective function. The total operating cost of the RIES-CSP can be described as the difference between the system operation expenses and the operating income [42]. The operating expenses of the system mainly include the system operation cost and the environmental cost. The operating income mainly refers to the income obtained by the system and the energy market for energy exchange, and the income obtained by participating in the IDR program.

MinC = COperation + CEmission − CRevenue

(7)

where C is the total operating cost of the RIES-CSP, yuan ; COperation is the system operation and maintenance cost, yuan ; CEmission is the system the environmental cost, yuan ; CRevenue is the system operating income, yuan . A. System operation and maintenance cost The operation and maintenance cost of the RIES-CSP system mainly refers to the necessary expenses for maintaining the operation of the system equipment and the cost of maintaining the energy balance of the system. And the system operation and maintenance cost mainly consists of three parts: the operation and maintenance cost of the system equipment, the fuel cost of the system equipment, and the cost of purchasing electricity and thermal energy from the urban energy network [43].

(3)

c PCSP (t )

is the thermal power of collector, kW; η1 is the photowhere thermal conversion efficiency; S is the total area of mirror, m2 ; Dt is the direct radiation index of solar illumination (DNI). The thermal converted by the thermal collecting device is directly used for power generation when the load demand is met, and is stored in the thermal storage system when the load is low for exothermic power generation at peak load. And the thermal storage and thermal release characteristics of the thermal energy storage system are expressed as follows:

COperation = CO − eq + CO − fu + CO − el + CO − th n

∑ PCCHP (t )Δt CCHP × LHVNG

= ∑ Ci Pi (t ) + ∂fuel δ i=1

n

+ ∑ [(IγE − dr , i + (1 − I) γE , i ) PEbuy _grid, i (t )] i=1

(8) C ⎧ PTES (t ) = PS − E (t ) ηc D ⎨ PTES (t ) = PS − T (t ) ηd−1 ⎩

where CO − eq is the operation cost of electric and thermal energy supply (4) Photothermal aggregation

C D (t ) is the thermal storage power of TES, kW; PTES (t ) is the where PTES thermal release power of TES, kW; PS − E (t ) is the thermal power directly used by the thermal collecting device to generate electricity, kW; PS − T (t ) is the thermal power delivered by the thermal collecting device to the TES, kW; ηc is the loss rate when charging; ηd is the loss rate when exothermic. The thermal storage system is always accompanied by energy loss in the process of energy storage. The calculation formula of thermal energy loss is as follows: C D QTES (t ) = (1 − ηs Δt ) QTES (t − 1) + (PTES (t ) − PTES (t ))

Power generation

G

(5)

where QTES (t ) is the thermal storage in the TES, kW; ηs is the thermal dissipation coefficient; Δt is the storage thermal and thermal release time interval, h. The output power of the CSP system is determined by the thermal power of the thermal collecting device and the TES. Therefore, the

Thermodynamic cycle Fig. 2. The basic principles of CSP system operation. 5

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equipment, yuan ; Ci is the operation cost of the energy equipment, yuan / kW; Pi is the output of the different energy equipment, kW; CO − fu is the operation cost of NG supply module, yuan ; ∂fuel is the NG price, yuan /m3; LHVNG is the low calorific value of Natural Gas, kWh/m3; CO − el is the power exchange cost between system and power grid, yuan ; CO − th is cost of thermal mitigation for systems and thermal company, yuan ; PEbuy _grid, i (t ) is the power that the urban energy network input energy to the system (Electricity and thermal energy), kW; γE − dr , i is the energy price of IDR, yuan /kWh; γE , i is the fixed energy price, yuan /kWh; I is the IDR coefficients, I = 1 indicates that the system participates in the IDR program; I = 0 indicates that the system does not participate in the IDR program.

n i=1

where and represent system energy sales revenue and IDR program revenue, yuan ; PEsell _grid, i (t ) is the power that the system input energy to the urban energy network (Electricity and thermal energy), kW; γE − dr , i is the energy price of IDR, yuan /kWh; γE , i is the fixed energy price, yuan /kWh; I is the IDR coefficients, I = 1indicates that the system participates in the IDR program; I = 0 indicates that the system does not participate in the IDR program. In order to quantify the economic benefits of system participation in IDR program, the IDR program income model is established in this paper through the DR elastic coefficient matrix, consumer psychology, statistics and exponential function fitting. The economic benefits of IDR are quantified as the cost of electricity and thermal saved after the system participates in IDR program [46]. ① System power cost savings are as follows:

In the economic operation optimization of RIES-CSP system, the environmental emission problem is the main factor affecting the efficient operation of the system. In the operation optimization model established in this paper, the power purchased from the urban power grid is green power, and the pollutants in the system mainly come from the pollutants generated during the operation of the power generation unit in the system. As the environmental problems become more and more serious, the RIES-CSP system not only has to pay the necessary pollutant discharge fees during the operation, but also pays a certain penalty fee in the case of excessive pollutant discharge. This paper establishes an objective function based on basic carbon emissions costs and ladder penalty fees [44].

CEmission

W

I

(10)

e−t CDR

e−t CRIES − Grid

B. System Environmental Cost

I

∑ [(IγE −dr,i + (1 − I) γE,i) PEsell_grid,i (t )] + ΔC e + ΔC t

=

n

n

e − C0e = ∑i = 1 Qe − Load1 − i γe − dr ,1 − i − ∑i = 1 Qe − Load − i γe − dr , i ΔC e = CDR

=

n ∑i = 1

(Qe − Load − i + ΔQe − Load − i ) γe − dr ,1 − i −

n ∑i = 1

=

(Qe − Load − i +

n ∑i = 1

−

n ∑i = 1

M ∑J = 1

Qe − Load − i γe − dr , ii

Δγ R iiE − e γe − dr , i i ∂ E − eQe − Load − i ) γe − dr ,1 − i e − dr , i

Qe − Load − i γe − dr , i

C0e

(11)

e CDR

where and are the power cost of the system before and after the electric load participates in the IDR program, yuan ; Qe − Load − i is the load before the system participates in the IDR, kWh; Δγe − dr , i is the change in retail electricity price after the IDR is implemented, yuan /kWh; γe − dr , i is initial retail price before the IDR is implemented, yuan /kWh; R iiE − e is the “Electric load-electric price” demand response elastic coefficient; ∂ E − e is the capacity ratio of elastic electric load. ② System thermal cost savings are as follows:

W

⎧ 0 < ∑ ∑ Pi (t ) ϖi, m Δt ∑ ∑ Pi (t ) ϖi, m θm Δt ⎪ i=1 m=1 i=1 m=1 ⎪ ⎪ ⩽ GS ⎪ I W I W ⎪ ⎪ ∑ ∑ Pi (t ) ϖi, m θm Δt + (GS1 − GS ) GS < ∑ ∑ Pi (t ) ϖi, m Δt = i=1 m=1 i=1 j=1 ⎨ ⎪ δm ⩽ GS1 ⎪ I W I W ⎪ ∑ ∑ Pi (t ) ϖi, m θm Δt + (GS1 − GS ) GS1 < ∑ ∑ Pi (t ) ϖi, m ⎪i=1 m=1 i=1 m=1 ⎪ ⎪ δm + (GS2 − GS1) λm Δ t ⩽ GS2 ⎩ (9)

n

n

t − C0t = ∑i = 1 Qt − Loadd1 − i γt − dr ,1 − i − ∑i = 1 Qt − Load − i γt − dr , i ΔC t = CDR

=

n ∑i = 1

=

(Qt − Load − i + ΔQt − Load − i ) γt − dr ,1 − i −

n ∑i = 1

−

(Qt − Load − i +

n ∑i = 1

where C0t

M ∑J = 1

n ∑i = 1

Qt − Load − i γt − dr , i i

Δγ R iiE − t γ t − dr , i ∂ E − t Qt − Load − i ) γt − dr ,1 − i t − dr , i

Qt − Load − i γt − dr , i

(12)

t and CDR

are the thermal cost of the system before and after the thermal load participates in the IDR program, yuan ; Qt − Load − i is the thermal load before the system participates in the IDR, kWh; Δγt − dr , i is the change in retail thermal price after the IDR is implemented, yuan /kWh; γt − dr , i is initial retail thermal price before the IDR is implemented, yuan /kWh; R iiE − t is the “Thermal load-thermal price” demand response elastic coefficient; ∂ E − t is the capacity ratio of elastic thermal load.

where CEmission is the environmental cost; Pi (t ) is the power of the emission source i at time t , kW; ϖi, m is the emission of the pollutant i from the emission source m per unit of electricity produced, kg/kWh; θm is the unit price of the pollutant m , yuan /kg; δm and λm are the ladder environmental trade price of the pollutant m , yuan /kg; GS is the upper limit of basic emission standards, kg; GS1, and GS2 are the standard discrimination of the ladder discharge penalty, kg.

3.2. Constraints

C. System Economic Benefits

(1). CSP operation constraints in the RIES

The electrical load and thermal load in RIES-CSP system are divided into two types: inelastic load and elastic load. The power consumed by inelastic loads does not change with energy prices, while the elastic load adjusts demand as energy prices change. In RIES-CSP system, the retail energy price signals can be used to guide users to change demand patterns and participate in IDR program, and the response to electricity price and thermal price reduction/increased load demand is simulated to increase/decrease virtual power generation output and Virtual thermal output, which increases the economy of the system operation. At the same time, the system can sell excess electricity and thermal to the urban energy network under the guidance of the energy price strategy [45].

According to the structural characteristics of the CSP system, the system mainly consists of a thermal collecting device, a thermal energy storage device and a generator. The components of the system must meet the operational characteristics constraints and system power balance constraints of the equipment during operation. The operational constraints of the CSP system mainly include the following three aspects [5,9,10]. ① Thermal transfer balance constraint C D c PTES (t ) + PS − E (t ) + ∂CSP − E PCSP − E (t ) − PTES (t ) ⩽ PCSP (t )

(13)

D PS − E (t ) + ∂CSP − E PCSP − E (t ) − PTES (t ) ⩾ 0

where 6

c PCSP (t )

is the thermal power of collector, kW;

(14) C PTES (t )

is the

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P. Jiang, et al. D (t ) is the thermal release power thermal storage power of TES, kW; PTES of TES, kW; PS − E (t ) is the thermal power directly used by the thermal collecting device to generate electricity, kW; PCSP − E (t ) is the thermal power required for start-up of the CSP plant, kW; ∂CSP − E is used to describe the operating state of the CSP system, ∂CSP − E = 1 means start, and ∂CSP − E = 0 means off. ② Thermal energy storage device constraint

max where PEmax − grid and PT − grid are upper limits of power that that system can exchange with the electricity grid and thermal grid, kW; PEmin − grid and PTmin − grid are low limits of power that that system can exchange with the electricity grid and thermal grid, kW.

D − csp D − load D PTES (t ) + PTES (t ) = PTES (t )

(15)

As with energy storage system, the capacity state of energy storage should always be kept within a certain range, as follows [20].

C PS − T (t ) + PSEB − T (t ) = PTES (t )

(16)

(4). EES device constraint in the RIES

SOCEES (t + 1)

D − load PTES (t )

where is the TES supplies thermal release power of the D − csp (t ) is the thermal release device supplies the thermal load, kW; PTES thermal release power generated by the CSP system, kW; PSEB − T (t ) is the thermal power delivered by the electric boiler to the TES, kW. ③ Generator set output constraint [27] min max PCSP (t ) ⩽ PCSP (t ) ⩽ PCSP (t )

PCSP (t ) − PCSP (t − 1) ⩽

up PCSP − tg

down PCSP (t − 1) − PCSP (t ) ⩽ PCSP − tg

1 = SOCEES (t )(1 − ħ) − ⎡ηEES − C REES − C PEES − C (t ) + REES − D ⎢ ηEES − D ⎣ PEES − D (t ) ⎤ Δt ⎥ ⎦ min SOCEES

(18)

where SOC (t + 1) and SOC (t ) are the states of capacity of EES at time t + 1 and t ; ħ is the self-discharge rate ; ηEES − C and ηEES − D are EES charge and discharge efficiencies ; REES − C and REES − D are charge and discharge max state variables of the EES, and REES − C + REES − D ∈ (0, 1) ; SOCEES and min SOCEES are upper and lower limits of the EES state of capacity.

(19)

min PCSP (t )

max PCSP (t )

is the minimum electric power of CSP, kW; is the where up maximum electric power of CSP, kW; PCSP − tg is the maximum upward down climbing rate of steam turbine generators for CSP, kW; PCSP − tg is the maximum downward climbing rate of steam turbine generators for CSP, kW.

C Ploss (t ) + PE − load (t ) + PTES (t )

(20)

M

⎧ ∑ ΔP =0 ⎪ t = 1 E − load

where Ploss (t ) is power loss, kW; PE − load (t ) is the electrical load power, C D (t ) and PTES (t ) are charge and discharge power of EES, kW; kW; PTES PWT (t ) is the output power of the Wind Turbines, kW; PCCHP − E (t ) is the electrical output power of CCHP, kW. ② Thermal power balance [12]

⎨M ⎪ ∑ ΔPT − load = 0 ⎩t=1

(27)

② Temperature constraint

(21)

Tmin (t ) < T (t ) < Tmax (t )

where PT − load (t ) is thermal load power, kW; PCCHP − T (t ) is the thermal output power of CCHP, kW; PEB (t ) is the output power of EB, kW; PTES, C (t ) and PTES, D (t ) are charge and discharge power of TES, kW, PT − grid (t ) is the thermal exchange power between RIES and urban thermal network, kW. ③ Natural Gas power balance

PG − grid (t ) + PE − G (t ) = PG − eles (t ) +

(26)

When the user participates in the electrical and thermal Demand Response, the electrical load and thermal load demand of each time period are adjusted according to the price, and the total load amount in one operation cycle is not changed. When the user adjusts the running state of the equipment, it should not only respond to the dispatch of the power grid, but also ensure that the comfort of its own electricity is not affected greatly. In the demand response model established in this article, temperature and power consumption are used to evaluate comfort degree before and after response to demand [16,18,19]. ① IDR load variation constraint

Energy balance means that the power supply in the system always meets the power demand. Energy balance constraints include electric, thermal and gas power balance. ① Electric power balance [15]

C D PT − load (t ) + PTES (t ) = PTES (t ) + PT − grid (t ) + PCCHP − T (t ) + PEB (t )

⩽ SOCEES (t + 1) ⩽

max SOCEES

(5). Integrated Demand Response constraint in the RIES

(2). Energy balance constraints in the RIES

D = PWT (t ) + PCSP (t ) + PCCHP − E (t ) + PE − gird (t ) + PTES (t )

(25)

(17)

(28)

where T (t ) is the temperature in the room, °C; Tmax (t) and Tmin(t) are the upper and lower limits of user's temperature demand for temperature control equipment, °C. ③ Operating time constraint The IDR load runs continuously in a certain time, and the running time constraints are as follows [32,46].

Pcchp (t ) ηcchp

1 Ton (x i ) ⩽ T ⩽ Ton (x i ) + ΔT (x i ) Xi (T ) = ⎧ ⎨ ⎩ 0 T < Ton (x i ) or T > Ton (x i ) + ΔT (x i )

(22)

where PE − G (t ) is the power of gas storage system, kW; PG − eles (t ) is NG load power of resident, kW; ηcchp is the utilization efficiency of NG in the CCHP system, %.

(29)

where Xi (T ) is the operational state of the transferable load; Ton (x i ) is the time the device starts running; ΔT (x i ) is the device scheduling period.

(3). Energy network power constrains in the RIES 3.3. Solution method Considering the safety of energy transmission pipeline and the economy of system cost, the exchange power between the system and external network must be controlled within a certain range [7,15]. max PEmin − grid ⩽ |PE − grid| ⩽ PE − grid

(23)

max PTmin − grid ⩽ PT − grid ⩽ PT − grid

(24)

In the model constructed in this paper, the profit of Demand Response program is a quadratic function of the change rate of retail energy price. In order to improve the calculation speed, the objective function is linearized by the method in document. In this way, the optimal operation model of integrated energy system with demand response is transformed into a mixed integer linear programming 7

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setting. (3). After all parameters are set, the simulation process is calculated and the results output mainly contain the objective function value and the optimization result of equipment.

problem. Considering that the linearized model still has some complexity, the Standard Genetic Algorithm with elite retention strategy (GA-ERS) is used to solve the model. Compared with traditional genetic algorithm, elite retention strategy is added. Elite retention strategy enables the best individuals in the parent population to be stored directly in the offspring population, avoiding the destruction of the best individuals in the process of cross-mutation [47]. Therefore, the standard genetic algorithm with elite retention strategy has the advantages of global convergence and fast convergence. The economic dispatching process based on standard Genetic Algorithm (GA) with elite retention strategy is shown in Fig. 3. According to the characteristics of GA algorithm, the parameters needed to be input in the simulation process mainly include system operation parameters and algorithm calculation parameters.

4. Case study 4.1. Case study description In this paper, a Regional Integrated Energy System with CSP system in Northwest China is selected as the research object. And the RIES-CSP system includes CSP station, wind power station, gas turbine (GT), electric energy storage (EES) and electric boiler (EB). The operation cycle is 24 h and the optimal scheduling cycle is 1 h. Table 2 shows the Techno-economic parameters of CSP plant (2.5 MW). The equipment parameters of CCHP system, WT, EES and EB are shown in Table 3 respectively. Table 4 is the basic carbon emission parameters and step carbon emission penalty parameters [48]. Fig. 4 shows the electrical and thermal load curves on a typical winter day, and the predicted values of typical daily output of renewable energy generator sets are shown in Fig. 5. The predicted values of typical daily IDR energy price are shown in Fig. 6. In addition, in

(1). The operation parameters, loads, energy prices, pollutant emission absorption and various cost coefficients are input as the basic data of simulation. (2). The algorithm parameters are set, the population size is set to 500, and the maximum iteration number is set to 200. Table 1 shows the Standard Genetic Algorithm with elite retention strategy parameter

Fig. 3. Flowchart of standard Genetic Algorithm flow with elite retention strategy. 8

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Table 1 The standard GA with elite retention strategy parameter setting. Parameters

Population size

Iteration number

Cross ratio

Variance ratio

Constant

Value

500

200

0.9

0.25

106

addition to the IDR energy price, the fixed thermal price is 0.125 yuan / kWh, and the Natural Gas price is 3.25 yuan /m3.

Table 2 Techno-economic parameters of 2.5 MW CSP plant. Techno-economic parameters

Value

Rated output power of the CPS Maximum ramping rate of the CPS Exothermic loss rate of TES Electrical conversion efficiency of CSP Maximum thermal storage capacity of TES

2500 kW 1200kw/h 3.5% 45% 25,000 kWh

4.2. Case study process In this section, the CSP system operation optimization model is simulated and analyzed. The objective of the simulation process is to minimize the total cost including operation cost, environment cost and operation benefit, and the basic data are load and energy price. Through the simulation of two different Demand Response modes, the optimal strategy of system load and electric and thermal equipment operation under different DR modes is studied. Fig. 7 shows the optimization simulation process of the RIES-CSP system. The simulation process of optimization is as follows:

Table 3 Conventional E-T unit technical and economic parameters in the RIES. Techno-economic parameters

Value

CCHP electrical efficiency CCHP thermal efficiency EES maximum charge power EES maximum discharge power EES charge/discharge efficiency Operation efficiency of EB Maximum ramping rate of the CPS

0.325 0.398 500 kW 500 kW 0.95 0.95 1500kw/h

(1). Enter basic parameters. Equipment units basic parameters participating in scheduling. Load data for a typical winter day. Natural gas price parameter and Demand Response price parameter. (2). Scene design.

Table 4 Basic carbon emission parameters and step carbon emission penalty parameters. Conventional emission

Excessive punishment

Pollutants

SO2

NOx

CO2

CO

NG Emission (kg/ 106 m3)

11.6

0.0062

2.01

0

Environmental value ( yuan /kg)

6.1308

26.00

0.0867

1.00

Basic emission limit (kg/h) Penalty standard/ yuan

≤5

5–7

7–10

≥10

0

0.16

0.26

0.35

According to the characteristics of energy demand, system structure and DR program in the system, this paper sets two simulation scenarios to optimize the operation of the system. (3). Operation Optimization and Results. According to the system operation model established in Section 3 of this paper, the GA algorithm is used to simulate the operation of the system in different cases designed in Step2. The simulation results obtained the optimal operation strategy of power equipment and thermal equipment in typical winter day of different scenes. (4). Discussion and analysis. The simulation results under different scenarios are discussed and analyzed. Firstly, the calculation method is verified. On this basis, the

Fig. 4. Electrical and thermal load curves on a typical winter day. 9

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Fig. 5. Full load output power of renewable energy on typical day in winter.

Fig. 6. Demand Response Electricity Price and Thermal Price.

4.3. Case study results

composition of system costs, carbon emissions and GT operating efficiency are analyzed. Finally, the confidence level of renewable energy prediction and sensitivity analysis of energy prices are carried out.

In order to verify the effectiveness of the proposed method, this section simulates the two typical Demand Response scenarios designed in Section 4.2. Through simulation, the output of renewable energy and the output strategy of electric heating unit under different DR scenarios are analyzed. In order to verify the effectiveness of the GA-ERS algorithm in solving the established model, this paper uses the traditional GA algorithm to optimize the total cost of the two cases to compare the performance of the two algorithms. The optimization results are shown in Table 5, and the fitness curve is shown in Fig. 8. It can be seen from Table 5 and Fig. 8 that the optimal total cost of GA-ERS is much better than that of GA algorithm, which indicates that the convergence speed and global search ability of GA-ERS algorithm are better than GA algorithm. In addition, GA-ERS algorithm is superior to traditional GA algorithm in calculation results and receipts. It can be clearly seen that GA algorithm falls into local optimum when solving the model established in this paper. The global search ability and convergence performance of GA-ERS algorithm are relatively strong, while effectively avoiding falling into local optimum, thus getting better results.

Case 1. On a typical winter day, electric load participates in Demand Response program, insufficient thermal of the system is supplemented by EB. The CCHP unit has a constant thermoelectric ratio and cannot be dynamically matched with the actual thermoelectric load. The strong coupling relationship makes the CCHP unit have limited peaking capability. There is no thermal exchange between the system and the urban thermal network, and the thermal load does not participate in Demand Response program. Electricity price implements IDR price, NG price is 3.25 yuan /m3. Case 2. On a typical winter day, both electric load and thermal load participate in Demand Response program. CCHP unit thermoelectric ratio can be adjusted to realize CCHP thermal electrolysis, so that CCHP dynamically adjusts thermal power output according to thermal power load in the system, and has strong peak-shaving capability. The system can exchange thermal with urban heat network. The Electricity price and the thermal price both implement IDR price, NG price is 3.25 yuan /m3.

10

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Fig. 7. Optimization simulation process of the SCP Integrated Energy System.

CSP systems to provide electricity and thermal and participate in the DR of the electricity market. The renewable energy system in the system is mobilized to actively participate in the power demand response while meeting the systems energy need. Fig. 9 shows the actual output and utilization rate of renewable energy generator in case 1. In this case, the CSP output is ideal, the average utilization efficiency of photovoltaic is 88.5%, and the utilization efficiency of wind power is 77%. According to the energy price of the IDR program and the scheduling strategy in Case 1, the purchase price and the sale price of electricity are TOU price, and the NG price is 3.25 yuan /m3. Based on the IDR strategy, the system load (Electrical load and thermal load) curve of case 1 is obtained (shown in Fig. 10 and Fig. 12), and the actual output curve of dispatching unit (Electricity supply unit and thermal unit) in case 1 is also obtained (shown in Fig. 11 and Fig. 13).

Table 5 Performance comparison of GA algorithms in different Cases. Cases

Algorithm

Best cost/yuan

Average cost/yuan

Iteration Number

Case 1

GA GA-ERS GA GA-ERS

65068.67 57951.11 60368.67 53400.59

69077.6 61634.55 64377.6 56934.55

71 58 68 57

Case 2

4.3.1. Optimization results in case 1 According to the energy supply model in Case 1, the GT and CSP systems are the main sources of electrical and thermal power within the RIES-CSP system. In this mode, the thermal supply in the system is rigid and the power supply is more flexible. The system prioritizes the use of 11

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Fig. 8. Comparison of iterative curves of different algorithms under different conditions.

Fig. 9. Actual output and utilization rate of renewable energy generator in case 1.

sources in the system include CCHP and EB, which together with TES ensure thermal balance within the system. As the system participates in the DR program of the power market, during the nighttime low electricity price period, the EB output is improved, the GT output is reduced, and the TES operation is more active, thereby ensuring the efficiency and economy of the system heating.

As shown in Fig. 11, in order to improve the system operation efficiency, the system weakens the unit output in the low electricity price period system. Through this coordination strategy, the system always maintains the maximum benefit when interacting with the external energy network. In Case1, since the thermal load does not participate in the IDR program, the thermal load curve does not change before and after the IDR implementation (shown in Fig. 12). In this case, the system does not exchange thermal with the urban thermal network, and the thermal supply in the system is always consistent and self-sufficient. The heat

4.3.2. Optimization results in case 2 In the energy supply model in Case 2, the power supply and thermal supply in the system are more flexible than in Case 1. Renewable energy 12

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Fig. 10. Response curves of electrical load in case 1.

Case 1, and the output of EES system is relatively reduced. In Case 2, the thermal supply of the RIES-CSP system is more flexible, the system can exchange thermal with the urban thermal network, and actively participate in the thermal market Demand Response. In this case, the thermal load curve has a large change compared with Case 1, especially in the period when the thermal demand is large and the thermal price is relatively high (11:00–22:00), the thermal load curve response degree is more obvious (Shown in Fig. 17). At the same time, during the period when the system thermal demand is low and the thermal exchange price is high (1:00–7:00 and 21:00–24:00), the output of the system GT is weakened, and the output of TES is strengthened (Shown in Fig. 18). In this way, the system could obtain additional benefits while keeping thermal balance in the system, and the operating cost of the GT in the system is also reduced. Ultimately, the overall economy of the system heating is improved.

systems (CSP and WT) and other energy supply units in the system are mobilized to actively participate in the Integrated Demand Response. In this flexible scheduling mode, the potential of the CSP and WT systems is completely released, and their actual output is near perfect. Fig. 14 shows the actual output and utilization rate of renewable energy generator in case 2. In this case, the CSP output is ideal, the average utilization efficiency of photovoltaic is 91.25%, and the utilization efficiency of wind power is 89%. According to the energy price of the IDR program and the scheduling strategy in Case 2, the purchase price and the sale price of electricity are TOU price, and the purchase price and the sale price of thermal are also TOU price, NG price is 3.25 yuan /m3. According to the IDR strategy, the system load (Electrical and thermal load) curve of case 2 is obtained (shown in Fig. 15 and Fig. 17), and the actual output curve of dispatching unit (Electricity supply unit and thermal supply unit) in case 2 is also obtained (shown in Fig. 16 and Fig. 18). As shown in Fig. 15 and Fig. 16, the electrical load curve and the actual output curve of electrical dispatching unit in Case 2 exhibits a different change from Case 1 after the system participates in the IDR program. In this case, since the electrical load and the thermal load participate in the IDR project at the same time, the electrical load is not only sensitive to the electricity price, but also affected by the system thermal balance and the thermal market. The electrical load demand response is a comprehensive response that is affected by both electricity price and thermal price. Compared to Case 1, the electrical load curve of Case 2 is significantly less reactive at night. This also leads to a large difference in the output strategy of power supply unit compared with

4.4. Case analysis and discussion 4.4.1. Results discussion A. Demand response results in different IDR modes According to the characteristics of the RIES-CSP and simulation results, it is clear that the supply of thermal load in the system has strong flexibility. On the one hand, the system can choose the EB system and CCHP to supply thermal for heating independently. On the other hand, the system can select CCHP and urban thermal network for

Fig. 11. Actual output curve of electrical dispatching unit in case 1. 13

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Fig. 12. Response curves of thermal load in case 1.

combined heating. The thermal load can be first provided by the heat source in the system, and the insufficient thermal is supplemented by the urban thermal network. Thermal load combines electric load with electrical load closely, making the load in the system sensitive not only to energy price, but also to other load. Based on the simulation results in different cases, the electrical load and thermal load response are shown in Table 6. According to the load changes before and after the IDR program (shown in Table 6), the Peak-Valley difference of electric load in Case 1 decreased from 3177 kW to 2916.79 kW, a decrease of about 2.49%, while the thermal load does not participate in the IDR. However, as the system in Case 2 participates in both the electricity market and the thermal market Demand Response, the electrical load and thermal load response in the system change significantly. The Peak-Valley difference of thermal load in Case 2 increased from 2752.96 kW to 3426.47 kW, an increase by about 10.07%, while the reduction in electrical load has dropped from 2.49% to 0.93%, with significant changes. It is clear that the load characteristics after DR are not only related to energy prices, but also to the impact of the energy supply model.

As shown in Table 7, the total cost of Case 1 is 57951.11 yuan , while Case 2 is relatively small at 53400.59 yuan . The operation costs in the two cases are 59945.49 yuan and 62854.76 yuan , respectively, and the environmental costs are 3107.71 yuan and 2910.18 yuan respectively. The operating expenses of the system in the two cases are not much different (63053.2 yuan and 65764.94 yuan ). However, because the system participates in the IDR project in Case 2, the system operation gains a large amount (As shown in the Fig. 19), which results in a small total cost of the latter. As shown in Table 8, the total maintenance cost of Case 2 is relatively large, which is about 8071.64 yuan higher than that of the Case 1. In Case 2, both the thermal load and the electrical load participate in the system demand response scheduling at the same time, resulting in frequent mobilization of the TES and the EES in the system, and the output of WT also crystallizes rated output, which increase the maintenance cost of the system.

B. Operation costs, environmental Cost and benefits in different Cases

According to the structural characteristics and energy supply characteristics of the RIES-CSP system studied in this paper, the pollutants in the system mainly come from the GT in the system, that is, the pollutants generated after the GT burns Natural Gas in the system. The operating conditions of the GT (Fig. 20 and Fig. 22) and system pollutant emissions (Fig. 21 and Fig. 23) in both cases are shown in the figures below. The conventional CCHP unit in Case 1 has a constant thermoelectric

C. GT operating conditions and environmental emissions in different Cases

It is because of the above reasons and the IDR response characteristics that there are significant differences in the operating state and optimization results of the system in both cases. Fig. 19 shows the results of energy exchange between system and urban energy networks. Table 7 shows the total costs comparison between different cases. Table 8 shows the system maintenance cost in both cases.

Fig. 13. Actual output curve of thermal dispatching unit in case 1. 14

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Fig. 14. Actual output and utilization rate of renewable energy generator in case 2.

by 18.37% (As shown in Fig. 23).

ratio and cannot be dynamically matched with the actual thermoelectric load. The strong coupling relationship makes the CCHP unit have limited peaking capability, and a large number of abandoned winds occur, and the actual output of the unit and the optimal output are seriously deviated. In this case, the output of GT is severely constrained by the thermal load in the system, so that the GT output during the lower thermal load cannot be completely released, and the unit is in a state of low efficiency operation. The actual load rate and operating efficiency curve of the GT in this case is shown in Fig. 20. During periods of low heat load (1:00–8:00; 23:00–24:00), the operating efficiency of GT is less than 20%. These make Natural Gas combustion insufficient and pollutant emissions serious (As shown in Fig. 21). The thermoelectric ratio of the CCHP unit in Case 2 is adjustable, and the CCHP thermoelectric coupling is realized, so that CCHP dynamically adjusts the thermoelectric power output according to the thermal load in the system, optimizes the energy configuration, and has the ability of peak clipping and valley filling. As shown in Fig. 22, the average load rate of the GT is higher than 50% during the period of low thermal load (1:00–8:00; 23:00–24:00), and the operating efficiency of the GT is at a higher level than that of Case 1, and the average operating efficiency is 30% ~50%. The main reason for this result is that GT and TES participate in the IDR program in the system, making the output of GT more affected by energy prices, and its output is always in a more flexible state. Due to the above-mentioned reasons, the pollutant emissions in Case 2 are much lower than those in Case 1. During the period of low thermal load, the pollutant emission of case 2 is about 45% lower than that of case 1, and the emission of pollutants is reduced

4.4.2. Sensitivity analysis A. Sensitivity analysis of uncertainty in renewable energy output The volatility and intermittent nature of wind power and photovoltaic output can have a negative impact on the economics of system operation. This section analyzes the degree of uncertainty of wind power and photovoltaic output for Case 1 and Case 2, and the impact on the operational efficiency of RIES-CSP system. Assuming that the confidence level is fixed at 0.9, the degree of blurring of wind power and photoelectric output is consistent, and Table 9 shows three fuzzy degrees of model parameter. And Fig. 24 shows the influence of uncertainty of WT and CSP output on system operation costs. According to the Fig. 24, as the fuzzy degree increases, the total system cost (T-C), operating cost (O-C), NG consumption and environmental cost (E-C) show the same trend in Case1 and Case 2. On the one hand, as the degree of uncertainty in wind power and photovoltaic output increases, the system will give priority to increase the output of GT. However, the output of GT is still limited by the expensive environmental emission publishing, so the system also needs to purchase some backup power from the power system. In this state, the cost of equipment maintenance, fuel and environment will increase significantly. On the other hand, due to the existence of TES and the increased GT output, the uncertainty of illumination has little effect on the power supply and thermal supply of the CSP. For thermal load and

Fig. 15. Response curves of electrical load in case 2. 15

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Fig. 16. Actual output curve of electrical dispatching unit in case 2.

of renewable energy output fluctuation on the electricity sales income, which also makes the change of revenue with the change of fuzzy degree in Case 2 only 1.16%.

electric load to participate in IDR program, the EES and electric heaters, the uncertainty of wind power and photoelectric output has little effect on them. However, there are significant differences in the trends in economic benefits (E-B) in both cases. The main reason for this phenomenon is the model of the system participating in the IDR program and the influence of fuzzy degree on the degree of interaction between the system and the external market. Compared with Case 1, the income of Case 2 includes the profit from selling surplus thermal in addition to the electricity sales. With the increase of fuzzy degree, the uncertainty of photovoltaic and wind power output increases. The fuzzy degree increases from M1 to M3 , and the economic income of the system in Case 1 decreases from 5102.09 yuan to 4017.25 yuan , with a decrease of more than 21%. However, the system revenue in Case 2 only decreased by 162.93 yuan , with a slight change. In Case 1, the system does not participate in the thermal trading in the market. When the uncertainty of renewable energy output increases, the system can only guarantee its own power demand by reducing the power delivered to the urban power grid, while avoiding the high environmental cost caused by the increase of GT output, so the economic benefits of the system will decrease with the increase of fuzzy degree. However, the electrical load and thermal load in Case 2 participate in the DR at the same time, which makes the energy transaction between the system and the external energy market strengthen, and the power generation and benefit of renewable energy increase. Especially when participating in the thermal market DR, the amount of thermal exchange is greatly affected by uncertainty of WT and CSP, and the huge thermal trading income makes up for the impact

B. Sensitivity analysis of Natural Gas price Based on the above analysis, GT plays an important role in system electricity supply and thermal, and is the main pollutant manufacturer in the system. Due to the double constraints of operating costs and environmental costs, natural gas price fluctuations have a significant impact on the GT operating mode, which will indirectly affect the operating status and cost of the system. This section studies the influence of different natural gas price changes on the operation results of the system operation costs. The results are shown in Fig. 25, where the natural gas price change rate is the ratio of each energy price change to its benchmark price (3.25 yuan /m3). In China, the price of natural gas has been kept at a high level, and the price of natural gas market conditions have a very large impact, which leads to a slight fluctuation of natural gas prices will have a huge impact on the operation of the RIES-CSP [10]. In Case 1, when the natural gas price dropped by 10% from 3.25 yuan /m3, the system Natural Gas consumption increased sharply, and the growth rate of natural gas consumption exceeds 53.8%, which shows the great influence of price fluctuation on CCHP system operation. At the same time, the total cost of the system increases slowly. The reason for different trends lies in the strict implementation of the ladder environmental emission penalty mechanism. Lower natural gas prices have led to an

Fig. 17. Response curves of thermal load in case 2. 16

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Fig. 18. Actual output curve of thermal dispatching unit in case 2.

Table 6 Electrical load and thermal load response in both cases. Situations Before DR After DR

Case 1 Case 2

E-load /kW T-load/kW E-load /kW T-load/kW E-load /kW T-load/kW

Peak of Load

Valley of Load

Peak-Valley Difference of Load

Reduction ratio

4977 3186.48 4356.79 3186.48 4334.88 3751.28

1800 433.52 1440 433.52 1569.6 324.81

3177 2752.96 2916.79 2752.96 2765.28 3426.47

– – 2.49% – 0.93% −10.07%

Fig. 19. Results of energy exchange between system and urban energy networks. Table 7 The total costs comparison between different cases. Cost/ yuan Operation cost (O-C)

Environmental Cost (E-C) Economic Benefits (E-B) Total Cost (T-C)

Power Cost Thermal Cost NG Cost Maintenance cost discharge Cost Penalty cost Selling electricity Selling Thermal DR Return

Table 8 System maintenance cost in both cases.

Case 1

Case 2

System maintenance cost/ yuan

Case 1

Case 2

1419.085 0 32299.02 26227.38 2690.19 417.52 1688.62 0 3413.47

1596.47 731.89 26227.38 34299.02 2558.29 351.89 3137.36 935.21 8291.78

WT CSP TES EES GT EB

13741.53 2706 2002.54 1497.43 5519.24 760.64

21,546 2791.8 2503.18 2601.47 4856.57 0

Total maintenance cost

26227.38

34299.02

57951.11

53400.59

system cost does not change much, and natural gas consumption is also declining. The reason for this phenomenon is similar to the decline in natural gas prices. Because, the operating state of the CCHP system is not only affected by the system scheduling strategy, but also strictly controlled by the ladder carbon emission mechanism.

increase in CCHP output, but this has also led to high environmental costs. The total operating cost of the system has not decreased significantly, but increased slowly. When natural gas prices rise, the total 17

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Fig. 20. Actual load rate and operating efficiency curve of GT in Case 1.

Fig. 21. Emissions per hour of pollutants in Case 1.

Fig. 22. Actual load rate and operating efficiency curve of GT in Case 2.

reduces thermal costs and environmental costs. Meanwhile, due to the lower thermal costs and less environmental costs, the total cost of system operation has also decreased. In addition, when natural gas prices began to rise, the total system cost and natural gas consumption showed a sharp decline. The high price of natural gas and the implementation of DR thermal price have severely restricted the activity of CCHP system. The system uses more flexible and cheap ways to supply electricity and thermal, which ultimately leads to a significant

Compared with Case 1, Case 2 shows a very different trend of change. As shown in the Fig. 25, when natural gas prices fall, the natural gas consumption not only increases but also decreases slowly, and the total system cost also decreases. In this case, the system participates in the electricity market and thermal market demand response at the same time, and the CCHP system can be flexibly adjusted. Although the price of natural gas has dropped dramatically, the output of CCHP has been limited due to the influence of urban thermal price, which greatly 18

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Fig. 23. Emissions per hour of pollutants in Case 2.

carbon emission mechanism (SCEM). It is validated in two different cases via the improved Standard Genetic Algorithm with elite retention strategy algorithm. The innovations and conclusions of work above could be concluded as follows:

Table 9 Fuzzy degree of model parameter. Fuzzy Degree

M1

M2

M3

Confidence

0.95

0.85

0.75

(1). The optimization model of RIES-CSP is established considering the basic carbon emissions costs and ladder penalty fees. Through the simulation results of two different cases, and the stepped carbon emission mechanism has a great influence to the optimization results. Due to the constraints of stepped carbon emission mechanism, the operation of GT is strictly constrained by environmental costs, and the optimal operation strategy of CCHP module is constrained by both SCEM and system scheduling. (2). The Integrated Demand Response mechanism is considered to establish optimization objectives and simulation strategies. In the objective function, a multiple integrated demand response model between electricity price, thermal price and electricity load and thermal load is established. According to the simulation results of two different DR modes, the load reduction rate of the system is 2.49% under the electric load DR mode. Under the IDR model, the electric load reduction rate was reduced to 0.93%, and the thermal load demand increased to 10.07%. (3). The effects of different Demand Response modes on system operation status and operation cost are considered. The simulation results of two different modes show that when the electricity and thermal dispatching units participate in the IDR program simultaneously, the system is in an ideal state. The total cost saving rate of

reduction in natural gas consumption and total operating costs. Although the price of natural gas has been greatly reduced, in order to avoid the generation of more pollutants, the output of GT is strictly limited, the consumption of natural gas is drastically reduced, and the total cost is slowly reduced. When natural gas prices rise sharply, the output of GT is severely limited in order to avoid generating more fuel costs, and natural gas consumption dropped sharply. At the same time, because the system can exchange thermal with the thermal market, the insufficient thermal in the system can be purchased from the urban thermal network when the thermal price is low, which eventually leads to a sharp decline in the total system cost. The reasons for the above phenomenon can be summarized as more flexible thermal supply mode, and the application of IDR mechanism in electricity supply and thermal supply of the system. 5. Conclusions In this paper, an optimization model of a Regional Integrated Energy System Concentrating Solar Power consisting of WT, CCHP, EES and CSP is built, which aims to get the minimum total operation cost while considering Integrated Demand Response (IDR) and stepped

Fig. 24. Influence of uncertainty of WT and CSP output on system operation costs. 19

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Fig. 25. Influence of NG price on system operation costs.

the system is 7.86%, the emission of pollutants is reduced by 18.37%, and the average operation efficiency of GT is higher than 70%.

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