Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures

Energy Conversion and Management 208 (2020) 112575

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Implementation of a price-driven demand response in a distributed energy system with multi-energy flexibility measures

T

Jide Niua, Zhe Tiana,b, , Jie Zhua, Lu Yuea ⁎

a b

School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, MOE, Tianjin University, Tianjin 300072, China

ARTICLE INFO

ABSTRACT

Keywords: Demand response Energy flexibility Distributed energy system Ancillary service Time-of-use electricity price

Distributed energy systems are a promising integrated energy technology due to their energy efficiency and environment benefits. However, the increasing complexity of distributed energy systems, integrated with variable renewable energy, requires more intelligent operational methods to achieve economic efficiency, and reduce negative effects on the power grid. In this study, multi-flexibility measures are used to facilitate interaction between a distributed energy system and the power grid. First, a mixed-integer and linear programming model is proposed for optimizing the dispatch of a distributed energy system with minimum operational costs. Then, the price-driven demand response is performed by coordinating flexibility measures optimally in the operation of a distributed energy system in Guangdong province, China. A detailed case study is conducted in which three types of flexibility measures are modeled, and their effects on end-users and power grid are discussed. The optimal results show that each flexibility measure can well response to the time-of-use price. The distributed energy system’s operating costs were reduced by 1.7–12.9% when individual flexibility measures were applied and 19.6% when all the flexibility measures were implemented. However, the price-driven demand response program significantly decreases the tie-line’s (i.e. the power line connecting the distributed energy system to the main power grid) power stability. Three types of ancillary services based on multi-flexibility measures in a constraint method to distributed energy system are proposed in this paper and optimized based on the facilitate smooth interaction between the power grid and a distributed energy system. The Pareto frontiers indicate that all the operational costs decrease along with the ancillary service. The technical and economic boundaries of each ancillary service are further determined, which can help distributed energy system operators make more informed operational decisions.

1. Introduction In recent years, distributed energy systems (DESs) have attracted considerable attention due to their promising energy efficiency and environmental benefits [1]. The use of DESs, especially those integrated with renewable energy, is also growing rapidly in China [2]. A distributed energy system (DES) can be defined as a local multi-input and multi-output energy system, consisting of a wide range of technologies, such as energy storage units, combined cooling, power and heating subsystem and renewable energy subsystem [3]. Different kinds of DESs are introduced, including cogeneration, tri-generation, and poly-generation, which are designed to meet varying types of energy demands [4]. Compared with a traditional energy system, DESs can employ a wider range of technologies, thereby offering the possibility to improve operational flexibility and lower operational costs. However, from the

⁎

power grid perspective, the DES functions like a single energy producer or load, and enhanced interaction with the power grid poses major challenges for Power System Operators (PSOs), which requires the DES to be highly flexible in its interaction with the power grid. Furthermore, the rapidly decreasing cost of renewable energy is increasing the penetration of renewable energy sources in DESs [5]. Because electrical systems require a real-time balance between supply and demand, more flexibility measures are needed to manage renewable energy uncertainties [6]. Generally, demand response (DR) programs have a great potential to unlock energy flexibility in a DES by implementing an implicit mode, also known as a price-driven mode, such as a time-of-use (TOU) tariff, real-time price (RTP) and critical-peak price (CPP), and an explicit mode, allowing a DES to participate the regulation of the power interacted with the main power grid [5]. Furthermore, DR programs also

Corresponding author at: School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China. E-mail address: [email protected] (Z. Tian).

https://doi.org/10.1016/j.enconman.2020.112575 Received 29 September 2019; Received in revised form 7 January 2020; Accepted 2 February 2020 0196-8904/ © 2020 Elsevier Ltd. All rights reserved.

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Nomenclature

Q WSO , Q WSI

Indices and sets

QiCC , PiCC , bin AC r AC RTS riCC , biniCC ,

, l, m e, g i

index of time step electricity and natural gas index of chiller number

Abbreviations CCHP AC CC PV ICE ARX TD RH SR OS ES RTS COP TOU DES MILP

Parameters

combined cooling heating and power absorption chiller centrifugal chiller photovoltaic internal combustion engine autoregressive model with eXogenous inputs dry-bulb temperature relative humidity solar radiation occupant schedule equipment schedule room temperature schedule coefficient of performance time-of-use distributed energy system mixed-integer and linear programming

cooling load at time τ electricity load at time τ lower and upper bounds of room temperature evaporation temperature condensation temperature CC coefficient used to describe the COP of CC 3 lower and upper bounds of chiller cooling energy output N PV number of PV modules P PV rated power of each PV module SR hourly solar radiation intensity ¯ SR solar radiation intensity for testing the PV efficiency Pr e electricity price at time τ natural gas price Prg time interval (1 h in this paper) BaI , BaO charging and discharging efficiency of battery BaI , BaO maximum charging and discharging rate Q Ba, Q¯ Ba lower and upper state of battery ICEe , 2ICEe, 3ICEe coefficient used to describe the electricity effi1 ciency of ICE ICEh , 2ICEh, 3ICEh coefficient used to describe the thermal effi1 ciency of ICE P ICE , P¯ ICE lower limit and upper limit of ICE power generation Q AC , Q¯ AC lower and upper bounds of cooling energy output AC AC AC coefficient used to describe the cooling efficiency of 1 , 2 , 3 AC P PV PV power output

CL EL ¯ RTS , RTS Teo, i, Tci, i, CC CC 1 , 2 , CC ¯ CC Qi , Qi

Variables

P grid V gas Q Ba P BaI , P BaO ICEe , ICEh ICE r P ICE bin ICE Q ICE Q AC AC

charging and discharging cooling energy of cooling storage i th centrifugal chiller cooling energy output i th centrifugal chiller cooling power (kW) on/off status of AC partial load rate of AC room temperature setpoint partial load rate of i th chiller on/off status of i th chiller

tie-line power at time τ natural gas consumed at time τ state of the battery charging and discharging power of battery electrical and thermal efficiency of CCHP partial load rate of ICE power output of CCHP on/off status of ICE the thermal output of CCHP the absorption chiller cooling energy output the efficiency of AC

play a pivotal role in the electricity market; they balance supply and demand by taking advantage of load flexibility [7]. DR is one of the demand side management (DSM) methods; it was introduced with the aim of mitigating system reliability problem and price spikes [8], and is currently defined as “changes in electricity use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity or to incentive payments designed to induce lower electricity use at times of high wholesale market price or when system reliability is jeopardized” [9]. In China, most provinces have implemented TOU pricing, and in 2015, a hybrid power system with both tiered and TOU pricing was established by the National Development and Reform Commission (NDRC) of China, which aims to achieve load shift more efficiently [10]. According to the literature review in [11], a DR based on TOU pricing can achieve a peak load shifting of approximately 10%. In the field of flexibility measures used in DR program, a large number of scientific studies can be considered. In the publication of Majidi et al. [12], a battery and a heat storage tank in a hybrid energy system were used to flatten the power load curve and minimize the total cost in the presence of DR program. In [13], with the aim of smoothing the power fluctuations, the residential electric heat pumps and a battery storage system were modelled and applied in a DR program. In [14], multi-type energy storage system including alkaline electrolyzer, hydrogen cylinder bundles, fuel cell and a battery electric vehicle were

used to reduce the peak load and operational cost of a grid-connected microgrid. The energy flexibility provided by building thermal mass is commonly suggested as part of the solution to alleviate some of the upcoming challenges in DR program [15]. In [16], the building thermal mass of a hall was used as a thermal energy store to response the flexible electricity price in Germany. The results showed that the building thermal mass was a valuable flexibility resource, which participate actively in load management by optimally controlling the HVAC system. Cui et al. [17] proposed a hybrid building thermal model for predicting the building temperature, which would be used to characterized the properties of building’s thermal mass and further leverage its power flexibility to provide building DR and peaking load shifting. In a previous study [18], the authors investigated the flexibility potential of building thermal storage and battery energy storage in a price-driven DR program. Compared with the single building, Wang et al. [19] developed a data-based modelling framework and Resistance-Capacitance model to evaluate the energy flexibility potential of building clusters. In this paper, these flexibility measures are classified into three categories and modeled in detail in Section 2.2. Based on this, the application of different flexible measures in DR program is analyzed to explore the response characteristics of different flexible measures. As the saying goes, every coin has two sides. The same is true for DR 2

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program, which may have conflicting perspectives between end-user and power grid operator [20]. From the end-user perspective, the motivation to participate in a DR program is to achieve economic benefits [21]. Therefore, price-driven DR programs are widely used by electricity companies to guide end-users to adjust their energy consumption profiles. In [22], a price-response model that incorporates predictive control was developed for operating floor heating systems with the aim of reducing operational costs. Beyond minimizing costs, the operational optimization of DES via a multi-objective approach to also reduce environmental impacts was analyzed. In [23], a DR program based on RTP was implemented by optimally scheduling heating and power hubs, and the economic and environmental objectives were achieved by transferring a percentage of the load from peak periods to off-peak periods. Furthermore, in [24], increasing a heating system’s average coefficient of performance (COP), achieving significant cost reductions, and reducing passive losses in heat storage were achieved by employing multi-source energy flexibility measures under fluctuating energy price. In addition to an electrical DR program, a thermal demand response program (TDRP) was implemented in [25] to reduce a multi-carrier energy system’s operational costs. Although a DR program is an effective solution to reduce operational cost, the current price-driven DR mechanism, which is commonly employed widely in China, may face many difficulties. One important purpose of implementing a DR program is to maintain a balance between the electricity supply and demand by, for example, offsetting fluctuation of renewable energy [13], managing the peak load [26], and providing a spinning reserve [27]. A TOU price-driven DR program is an indirect load control method, which motivates the end-user to make full use of flexible resources and, thus, reduce operational costs. However, the application of flexibility measures in a TOU price-driven DR program that only considers end-user interest may decrease the stability of a power system [18]. The results presented in [28] indicate that compared to the demand curve under a static pricing program, a TOU price-driven DR reduces the overall electricity demand during peak hours but creates a new and much higher demand peak during off-peak hours. Moreover, according to a study conducted by Bartusch [29], of 500 households using a Swedish power system, only 50 participated in the DR program. This means the households’ willingness to voluntarily participate in a DR program was not strong, especially when the comfort level is sacrificed, and little money is saved on electricity bills. A DES is typically installed near its end-users and configured energy generators, storage and distributed technologies [30]. Therefore, supply- and demand-side flexibility measures can both be applied in the optimal dispatch of DESs’ to simultaneously provide controllable ancillary services for the power grid and lower the end-users’ operational costs. Because a DES is an integrated energy system with centralized ownership, performing direct load control combined with price-driven DR is feasible. However, the power quality of distributed power grids may be negatively impacted by connecting a DES to them, especially when the penetration of renewable energy in the DES is high. Direct [31] and indirect [32] load control are DR methods commonly adopted by operators to maintain the stability of a power system. A modified coordinating control solution is proposed in [13] to manage distributed heat pumps used to stabilize tie-line power fluctuations. The authors found that coordinating the control of heat pumps with a DR program can significantly reduce the need for battery storage and improve power quality. In [33], a workload scheduling model and an uninterruptible power supply (UPS) were integrated and used as a flexibility measure to smooth the tie-line power fluctuations of a data center energy system with a high renewable energy share. Olivella-Rosell et al. [34] discussed a new energy aggregator called smart energy service provider (SESP) to schedule flexible energy resources and balance energy generation and demand, which reduces the negative impact of distributed energy systems. In [34], three approaches were introduced to meet distribution operator requests, which include curtailing power generation by reducing or disconnecting flexible generators, achieving up and

down power regulation by controlling the charging and discharging schedules of batteries, and changing both the curtailable load and shiftable profile load demand. A mixed-integer and linear program (MILP) model was formulated in [34] to coordinate the abovementioned flexibility measures optimally. These studies illustrate that DR programs are an effective solution, from the perspective of power grid benefits, to provide frequency regulation reserve services for power grids [35] and smooth grid power fluctuations due to the high penetration of renewable energy [36]. The works described above reveal that applying flexibility measures to energy systems could benefit different stakeholders by, for example, reducing end-users’ operational costs, and maintaining the operational stability of power system. It is believed that employing a DR program is a win-win plan, benefiting utility companies, transmission and distribution system operators, and end-users [11]. Neves et al. [20] compared differences in DR optimizations from both the power grid manager’s and prosumer’s viewpoint, respectively. The results show that although optimizing controllable loads from either the grid manager’s or prosumer’s viewpoint could lead to production cost savings and increasing renewable energy shares, while more severe final load fluctuations were observed. A contract is commonly signed to prescribe the total energy requirement, power demand scope, and most preferred power consumption to facilitate cooperation between the DES and main power system in a price-based DR program. In [37] and [38], the electricity load was categorized as either an inelastic load or an elastic load and then the DR program was employed to regulate the elastic load. As the elastic load was achieved by sacrificing the end-users’ comfort, the DES’s operational costs, or both, a constant economic coefficient was employed to calculate the dispatching cost [39]. Various measures are used in DESs to provide flexibility for the power grid and end-users, and prior studies have made significant advancements in this area. However, few studies have focused on investigating the flexibility potential of DESs by simultaneously considering the end-user interest and the provision of ancillary services to the power grid. Apart from the flexibility measures in DESs, the built-in energy flexibility in buildings is recommended as an effective solution to stabilize energy grids, allowing for DSM to control load, and thereby alleviate some of the upcoming challenges in DR programs [40]. However, an in-deep understanding of how to model the flexibility of buildings and optimally dispatch building energy flexibility when combined with other kinds of flexibility measures in DESs has not been developed. In this context, the aim of this paper is, thus, to investigate the optimal operation of a DES while considering multi-flexibility measures and a price-driven DR program to achieve operational costs reduction and facilitate interaction between DESs and power grid simultaneously. The main features of this work are as follows: (1) How to model different flexibility measures? Regarding the modelling of single components, different flexibility measures are introduced and modelled, which includes flexibility measures based on energy substitution, energy storage and buildings’ thermal mass. (2) What is the impact of each flexibility measure on end-user and power grid in a price-driven DR program? A DR program considering the above three types of flexibility measures in a DES is formulated as a MILP model, and the potentials of different flexibility measures are further investigated. Meanwhile, the tie-line power flow ramping rate and the optimal operation cost are employed to evaluate the impact of the price-driven DR on power grid stability and end-user, respectively. (3) How to improve the friendly interaction between end-user and power grid? Three types of DR ancillary services for power grid are proposed. Theε-constraint method is employed to determine the Pareto frontier between ancillary service’s objective and end-users’ operational cost minimization, which intends to relieve the conflicting optimization DR goals between end-user and power grid operator. 3

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The reminder of this work is organized as follows. Section 2 describes the mathematical formulation of the flexibility measures and optimization problem for DES operation, and in Section 3, the case study is presented, and then the dynamic economic dispatch results are analyzed in Section 4. The effect of economic dispatch on power grid stability, the ancillary services and the applications of this study are discussed in Section 5. In Section 6, the paper’s main findings are summarized, and directions for future research are provided.

potential of the DES system, three other objectives will be presented in section 2.2.4, which can be regarded as ancillary services provided to the power grid. The multi-objective function will be managed by theεconstraint method [42], and the results will be discussed in Section 5.2 in detail. The dispatch cost is formulated as follows: T

min

(P grid

Pr e + V gas

Pr g)

(1)

where P grid is the tie-line power at , kW. Pr e is the electricity price at , $/kWh. V gas is the amount of natural gas consumed by the internal combustion engine at , m3/h; Prg represents the natural gas price, $/Nm3; is the length of time interval (1 h in this paper).

2. Problem modeling and formulation This section describes DES configuration and formulates the DES dispatch model, including an objective function, models of flexibility measures, constraints and three types of ancillary service models.

2.2.2. Modeling of flexibility measures (1) Flexibility measure based on energy storage The battery energy storage system’s operational conditions are subjected to constraints (2)-(4). Eq. (2) guarantees energy balance in battery storage. The charged and discharged power at time are constrained to be below a specific ratio of the battery’s capacity (Eq. (3) and Eq. (4)). The status of the battery is limited to a specific range, as described in Eq. (5).

2.1. The distributed energy system Fig. 1 depicts an abstract energy flow diagram of a DES. The energy inputs include the power grid, the natural gas grid, and solar energy. The energy conversion components mainly include electro-thermal conversion equipment (i.e., a centrifugal chiller (CC)), a combined cooling heating and power system (CCHP) consisting of an internal combustion engine (ICE) and an absorption chiller (AC)), energy storage equipment (i.e., battery and water storage), and the photovoltaic (PV) system. Two types of energy (i.e., electricity and cooling energy) are provided by the DES, and there are several kinds of flexibility measures in the DES. The battery and water storage in DES can shift the energy demand during one period to another period. Furthermore, the CCHP system can change the tie-line power profile through energy substitution, and buildings’ thermal mass can be used for thermal energy storage by modifying the heat delivery trajectory. The flexibility measures are marked with the red triangle symbol.

Q Ba = Q Ba1 (1 -

P

BaI

Q

P BaO

Ba

Q Ba

Q

+ P BaI

P BaO

BaI

BaO

(2) (3)

BaI

Q Ba Ba

Ba loss )

(4)

BaO

¯ Ba

(5)

Q

Ba is the electrical storage loss where Q is the stage of charge, kWh. loss BaO BaI ratio. P and P are the charging and discharging power, kW. BaI and BaO are electrical charging and discharging efficiencies. BaI and BaO are the charging and discharging rates. The battery’s state of charge is constrained between Q Ba and Q¯ Ba . These constraints on the cooling energy storage system are similar to those of the battery energy storage system, so they are not redefined here. (2) Flexibility measure based on energy substitution The CCHP system can supply flexibility services in two ways: controlling tie-line power through the internal combustion engine and adjusting the electric chiller’s power vis the absorption chiller. The generation efficiency and thermal efficiency of a CCHP system are functions of its partial load rate, described in Eq. (6), Eq. (7) and Eq. (8).

Ba

2.2. Price-driven dispatch model In this section, a mixed-integer and linear programming (MILP) model is used to optimally dispatch of a DES. The mathematical model is coded on the GAMS platform and solved by the Cplex solver [41]. 2.2.1. Objective function The optimization objective is to minimize the operational costs under the TOU pricing tariff. To further investigate the flexibility

r ICE = P ICE P¯ ICE ICEe ICEh

(6)

=

ICEe ICE 2 (r ) 1

=

ICEh ICE 2 (r ) 1

+

ICEe ICE r 2

+

ICEh ICE r 2

+ +

ICEe 3 ICEh 3

(7) (8)

where and are the gas to electricity and gas to heat efficiency of ICE. r ICE is the partial load ratio of ICE. 1ICEe, 2ICEe, 3ICEe and ICEh , 2ICEh, 3ICEh are coefficients used to fit the electricity and heat 1 efficiency, respectively. The power generation (P ICE ) of ICE can be calculated by Eq. (9) and constrained by the lower limit ( P ICE ) and upper limit (P¯ ICE ) of ICE power generation, as presented in Eq. (10). A binary variable (bin ICE ) is introduced in Eq. (10) to describe the on/off status of the CCHP system. ICEh

ICEe

P ICE = V gas bin

ICE

P ICE

ICEe

P

ICE

(9)

P¯ ICE

(10)

where V is the natural gas consumption of ICE. The thermal output (Q ICE ) of ICE can be calculated by Eq. (11). The cooling energy (Q AC ) generated by the AC is constrained by Eq. (12), in which efficiency ( AC ) is defined as a cubic function of the partial load rate. Generally, the operation of AC is also constrained by upper (Q¯ AC ) and lower (Q AC ) bounds, as shown in Eq. (14). bin AC is a binary variable used to represent the on/off status of AC. gas

Fig. 1. Schematic layout of a distributed energy system with integrated electricity and heating/cooling supplies and with multi-flexibility measures. 4

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Fig. 2. Three ancillary service models.

Q ICE = V gas

ICEh

(11)

Q AC

AC

(12)

AC

Q ICE =

AC AC 2 ) 1 (r

bin AC Q AC

Q AC

+

AC AC 2 r

+

AC 3

building’s design parameters. Generally, a mechanism-based building thermal model is built using algebraic differential equations, and the nonlinear model results in a computation problem. In this paper, a four step-ahead linear autoregressive model with exogenous inputs (ARX) is employed to predict the dynamic cooling load. The authors have previously demonstrated the ARX model in [18] in detail. Therefore, only a compact ARX model is defined in Eq. (15) in this paper. The specific work about construction and identification procedure of the ARX model could refer [18].

(13)

Q¯ AC

(14)

are coefficients where r is the partial load ratio of AC. of the AC efficiency curve. Eqs. (7), (8), (12) and (13) are non-linear, so the piecewise linearization method described on the Gurobi website [43] is used. (3) Flexibility measure based on building’s thermal mass In this paper, a virtual energy storage unit based on building’s thermal mass is introduced to provide demand-side flexibility. Energy flexibility from a building’s thermal mass can be achieved by modifying the heat delivery trajectory, which can be implemented via two measures. Depending on the adjustable thermal comfort range, it can (1) directly postpone cooling for a certain period without jeopardizing the thermal comfort, and by utilizing a building’s thermal mass, it is possible to (2) pre-cool within a comfortable range and thereby prolong the chiller shutdown or chiller power reduction period. The energy flexibility of a building’s thermal mass is a complex topic where the available energy flexibility is heavily dependent on, for example, weather conditions, the equipment/occupant schedule, and the AC

AC 1 ,

AC 2 ,

AC 3

6

CL =

3

al CL l=1

l

+

bm u

m,

u = {TD , RH , SR, OS , ES, RTS }

m=0

(15) where CL is the predicted cooling load. u m are the previous input data on which the current cooling load depends. In this paper, the drybulb temperature (TD), relative humidity (HD), solar irradiance (SR), occupant schedule (OS), equipment schedule (ES) and room temperature setpoint (RTS) are considered in the ARX model. ¯ ) and The room temperature is constrained between the upper (RTS lower (RTS ) temperature bounds, which is described in Eq. (16), to ensure thermal comfort. Furthermore, while the building is occupied, the room temperature difference between adjacent moments should not exceed 2.5 °C. This constraint is used to maintain the indoor comfort level, and is described in Eq. (17). 5

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Table 1 Time-of-use electricity and natural gas prices in Guangdong province. Natural gas price ($/m3)

Electricity price ($/kWh) Periods

Time interval

Price

Valley-price period Flat-price period Peak-price period

23:00~07:00 (next day) 07:00~09:00, 12:00~14:00, 17:00~19:00, 21:00~23:00 09:00~12:00, 14:00~17:00, 19:00~21:00

0.0502 0.1004 0.1656

RTS - 2.5

RTS

(16)

¯ RTS

RTS +1

RTS

COPi, =

(17)

2.5

CC biniCC , Qi

i

i

BaI PiCC , (1 + ) + P

AC QiCC + Q WSO = CL + Q WSI , + Q

(18)

P grid

Table 2 Technical parameters of dispatchable equipment in the distributed energy system [18,25,45]. Value

CCHP subsystem

P ICE , P¯ ICE Q AC , Q¯ AC

33 kW, 660 kW

Energy storage

Q Ba, Q¯ Ba

240kWh, 1080kWh

BaI , BaI ,

Ba loss QWs ,

Electrical chiller Transformer PV

BaO BaO

WsO

WsI ,

WsO

Ws loss QiCC , P¯TF

CC Q¯i

P PV ¯ SR pv

CC CC 2 ri,

+

CC 3 )

(21) (22)

SR ¯ SR

pv

(23)

P¯ TF

(24)

35 kW, 700 kW 0.25, 0.25 0.95, 0.95 0.05

Q¯ Ws

WsI ,

N PV

CC Q¯i

+

2.2.4. Ancillary service model A simple energy flexibility model is typically used to evaluate the capacity and value of flexible resources [39]. Moreover, a thorough understanding of the technical limitations and kinds of ancillary services used to provide energy flexibility is lacking. Consequently, three types of ancillary services for the power grid are proposed. The multiobjective optimization method is used to determine the technical and economic boundaries. The results will be discussed in Section 5.2. The first kind of ancillary service is proposed based on the consideration of peak power curtailment (AS_PPC), which is defined as Eq. (25) and shown in Fig. 2 (a) schematically. The second is proposed based on a ramping rate constraint (AS_RRC), which is formulated as Eq. (26) and further interpreted in Fig. 2 (b). The third is to customize the tie-line power profile with an acceptable deviation (AS_CPP), which is defined as Eq. (27) and described in Fig. 2 (c). The DES’s dispatch objective is still to minimize operational costs, and theε-constraint method [42] is employed to investigate the abovementioned ancillary services.

(20)

Parameters

CC CC 2 1 (ri, )

Tie-line power is constrained by the transformer’s capacity (P¯TF ).

where P grid is electric power imported from the power grid (i.e., the tieline power). P PV is electric power generated by the PV system. PiCC is , electric power consumed by the CC. QiCC , is cooling energy generated by the CC. Q WSO and Q WSI are the discharging and charging cooling energy of water storage (WS). The CC’s electricity consumption is calculated based on COPi, , which varies with the partial cooling load ratio (riCC , ), and a modified empirical model based on [44] is adopted, as shown in Eq. (21). The operational constraint on the CC is described in Eq. (20), in which the coefficient of performance (COPi, ) is defined by Eq. (21), depending on the partial load rate (riCC , ), evaporation temperature (Teo, i, ) and condensation temperature (Tci, i, ). The evaporation temperature of the chiller is set to be constant (7 °C in this paper), and the condensation temperature is determined by the outdoor air wet-bulb temperature. Similarly, Eq. (22) guarantees that the CC’s cooling output is constrained within a reasonable range. The binary variable (biniCC , ) is used to control the CC’s on/off status.

Equipment

QiCC ,

P PV = N PV P PV

(19)

CC QiCC , = Pi, COPi,

Teo, i, ( Teo, i,

The maximum power output of the PV system is calculated by Eq. ¯ are rated para(23). Fixed efficiency is employed. N PV , P PV , and SR meters, representing the number of installed PV modules, the rated power per PV module under the test condition and the solar radiation value under test condition (1000 W/m2), respectively. SR is the actual hourly solar radiation, W/m2.

2.2.3. Energy balance and other equipment-related operational constraints The electrical and cooling energy balances are shown in Eq. (18) and Eq. (19), respectively. The electric load (EL ), in this paper, excludes the CC’s electricity consumption.

P grid + P PV + P BaO + P ICE = EL +

Tci, i,

0.6420

300kWh, 2700kWh 0.17, 0.17 0.95, 0.95 0.05

53 kW/1055 kW 2500 kW

Fig. 3. Thermal and electrical efficiencies of an internal central engine (red and black dashed lines), absorption chiller efficiency (cyan dashed line), and centrifugal chiller’s coefficient of performance (yellow dashed line) [46]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

1714

310Wp

1000 W/m2 0.9

6

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described above affect operational costs and power grid fluctuation, optimal dispatch results are presented from five case studies. These case studies are presented in Section 3.1, and the parameters and input data used in the dispatch model are presented in Section 3.2. 3.1. Case study description As a case study for this paper, the dispatch of a DES for five industrial buildings in Sunwoda Electronic Co., Ltd, industrial park in Guangdong province (N 20o13′, E 109o39′) is investigated. All the buildings in the industrial park are used for Li-ion battery production. Cooling is required during most months of the year due to the subtropical climate and the massive heat dissipation of machines in the buildings, and there is no heat demand for domestic hot water or space heating in the industrial buildings. A centralized DES is installed in this industrial park to cover the cooling and electrical demands by generating the necessary amounts of energy and distributing it to individual buildings. The candidate technologies for the DES are described in Section 2.1 and shown in Fig. 1. Additionally, PV panels are installed on the buildings’ roofs and connected to the power grid. In this case, the DES can import electricity from the power grid, but does not allow exporting locally generated electricity to the power grid. A DR program driven by the TOU pricing tariff is applied to investigate the DES’s energy flexibility. As described in Section 2.2.2, three flexibility measures are modeled. In this section, the effects of different flexibility measures under a TOU price-driven DR program are analyzed step-by-step for five cases. The five comparative cases are present as follows: Case 1: optimize the dispatch strategy for minimizing the DES’s operational cost without FM_ES, FM_CCHP, and FM_VS. In this case, the battery’s and cooling energy storage’s capacities are set to 0. A high natural gas price is used (200 $/m3 in this paper); thus, the CCHP will not be activated since the objective is cost minimization. The room temperature schedule is maintained at the desired level, as shown by the blue line in Fig. 4 (f), and thus, the cooling load is determined, as shown in Fig. 4 (g). Case 2: optimize the dispatch strategy for minimizing the operational cost with FM_CCHP. This case is mainly used to test the energy flexibility of CCHP system. The natural gas price used in this case is presented in Table 1. Case 3: optimize the dispatch strategy for minimizing the operational cost with FM_ES. This case is mainly used to test the energy flexibility of energy storage (i.e., battery and cooling storage in this paper). The technical parameters of energy storage are listed in Table 2. Case 4: optimize the dispatch strategy for minimizing the operational cost with FM_VS. This case is mainly used to test the energy flexibility of virtual energy storage with the building thermal mass. The room temperature schedule can be adjusted within the comfort range, as depicted by the solid and dotted red line in Fig. 4 (f). Case 5: optimize the dispatch strategy to minimizing the operational cost by taking FM_CCHP, FM_ES and FM_VS into consideration synergistically.

Fig. 4. Forecasted inputs of the ARX model and cooling load: (a) outdoor air temperature, (b) relative humidity, (c) horizontal solar radiation intensity, (d) occupant schedule, (e) equipment operational schedule, (f) room temperature schedule, (g) cooling load.

P grid

P

(25)

P grid +1

RRgrid

P grid

grid

P

grid

P grid

± P

¯ grid RR

(26)

grid

(27)

grid

where P denotes the allowable tie-line peak power, constrained by the ¯ grid are the lower and upper transformer’s capacity, kW; RRgrid and RR grid ramping rates, respectively, kW/h; P is the customized tie-line power, and denotes the allowable fluctuation deviation, kW. 3. Case description In this section, to demonstrate how the flexibility measures Table 3 Parameters of the autoregressive model with exogenous inputs (ARX) model. Items

0 1 2 3 4 5 6 Constant

b

a

TD

RH

SR

OS

ES

RTS

42.21 −1.090 27.36 −27.08 — — — −262.49

9.547 −14.31 23.14 −13.82 — — —

0.078 0.063 0.067 −0.086 — — —

45.36 130.12 16.79 −31.98 — — —

1033.49 91.36 −277.78 −255.27 — — —

−175.60 96.66 13.77 25.30 — — —

7

— −0.025 0.055 0.115 0.041 0.040 0.009

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Fig. 5. Forecasted electricity load and photovoltaic (PV) power output.

3.2. Input data The TOU price is listed in Table 1. The technical parameters of the equipment are listed in Table 2, along with some data collected for a study presented in [18]. The COP and efficiency models are used as described in [44] and [46], and the coefficients are then fitted by the equipment sample data. The COP and efficiency curves of different types of equipment are plotted in Fig. 3, and a piecewise linearization method is used to linearize these conic models. The ARX model’s parameters are presented in Table 3. Fig. 4 (a)-(f) shows the one-week inputs of the ARX model. The forecasted cooling load is shown in Fig. 4 (g), and it should be noted that the cooling load is forecasted under a given room temperature schedule. However, during the DR program’s implementation, the indoor temperature schedule is adjustable, which is limited within upper and lower bounds, as shown by the red line in Fig. 4 (g). Therefore, the cooling load will also change accordingly because it is an elastic load. Fig. 5 shows the forecasted electric load and maximum power output of the PV system. Due to the limit roof area available in this case study, the maximum PV installation capacity is 0.531 MW, which is far lower than the electric load (EL ). 4. Dynamic economic dispatch results The dispatch results of the DES’s electricity generation in Cases 1–5 are shown in Fig. 6. A comparison of Case 1 and Case 2 reveals that the CCHP always tracks the peak electricity price well (i.e., reducing the tie-line power by increasing the CCHP system’s power output) In addition, the tie-line power in Case 2 is always less than or equal to that of Case 1, which means that DR, through an energy-substitution flexibility measure, can efficiently avoid new power-demand peak-shock in the valley-price period. A phenomenon wherein the “TOU pricing program did reduce the overall electricity demand during peak hours, but created a new and much bigger demand peak during the off-peak hours” was reported in [11] and is also observed in Case 3, in which the flexibility measure based on energy-storage is used. As shown in Fig. 6 (c), the fluctuation of tie-line power in Case 3 is more severe than that in Case 1, and the peak demand in Case 3 (1587 kW) is higher than that in Case 1 (1263 kW). Fig. 6 (d) shows the DES’s generation dispatch results, in which energy flexibility based on a building’s thermal mass is considered. It can be observed that tie-line power increases and decreases sharply when the electricity price changes from a valley-price period to a flatprice period, as shown by the dotted circles. Additionally, in other periods, the tie-line power pattern in Case 4 is almost the same as in Case 1. Fig. 7 further interprets the virtual energy storage effect of a building’s thermal mass. In Case 4, the room temperature setpoint was reduced prior to the flat-price/peak-price period, mainly to charge the cooling energy in the building’s thermal mass during the valley-price period and then discharge it during the flat-price/peak-price period. Compared to Case 1, the room temperature setpoint in Case 4 rises

Fig. 6. Hourly dispatch results of electricity generation in a typical week in July and the electricity price profile for comparison.

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Fig. 7. Comparison of room temperature schedules, cooling loads, and cooling power between Cases 1 and 4.

significantly during the occupant period, which means the building’s thermal mass can provide significant heat storage capacity by adjusting the room temperature setpoint within the comfort zone. Because a building’s cooling system consumes a considerable amount of energy [47], harnessing buildings’ thermal mass is a promising solution to increase flexibility in a DR program. Because the room temperature schedule in Case 1 and Case 4 is different, it will inevitably result in differences in the cooling load and, thus the CC’s power consumption. Differences in cooling loads and cooling power between Cases 1 and 4 are shown in Fig. 7 (b). During the valley-price period, the cooling load and cooling power in Case 4 are all higher than in Case 1, while it is the opposite during the peakprice/flat-price periods. Fig. 6 (e) shows the generation dispatch results of the DES when all three flexibility measures are applied to the DR program. The results demonstrate that each measure can respond well to the TOU price and reduce operational costs, while in Cases 1–4, the tie-line power fluctuation is more pronounced. Fig. 8 summarizes the operational costs of Cases 1–5 and the operational cost savings achieved in Cases 2–5. In can be observed that introducing the FM_CCHP into DES (Case 2) achieves the most significant operational cost savings, decreasing 12.9% compared to Case 1. Certainly, the CCHP system’s energy flexibility potential and the cost savings are closely related to the natural gas price. The operational cost decreases 5.4% (Case 3) and 1.7% (Case 4) by employing the FM_ES and FM_VS in the DES, respectively. However, a larger operational cost reduction was observed in [18] by using building thermal storage, which shows the energy flexibility potential of FM_VS is not constant and hardly determined, as it is closely related to the thermal parameters of building envelopes, the inner heat sources and weather conditions. Operational cost savings of 19.9% can be achieved by applying the three flexibility measures individually, while a 19.6% operational cost saving is obtained when the three flexibility measures are combined in Case 5. This finding indicates that the three flexibility measures have different priorities in the DR program.

Fig. 8. Operational costs in Cases 1–5 and the cost savings achieved in Cases 2–4 compared to Case 1.

discussed further, from the perspective of a TOU price-driven DR program’s effect on the power grid and the ancillary services provided by end-users to facilitate interaction with the power grid. 5.1. The effect of a demand response program on the power grid For power transmission and distribution system operators, DR is a powerful tool used to balance supply and demand, especially since the low-voltage power grids are becoming carriers of bi-directional electricity flows with the increased penetration of DES. However, because the objective is only cost minimization for the end-user, implementing a DR program based on the TOU pricing tariff may decrease power grid stability and reliability. In this section, a the tie-line power flow ramping rate is introduced to evaluate the stability of power exchange between the power grid and DES, which is defined as follows [13]:

P grid +1

P grid

5. Discussion

P¯ grid =

The optimal dispatch results presented in Sections 4 indicate some interesting findings were obtained. In this section, these results are

The hourly tie-line power flows and ramping rates in Cases 1–5 are shown in Fig. 9 (a) and (b), respectively. The results reveal that the tie9

(28)

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Fig. 9. Tie-line power fluctuation in Cases 1–5.

line power flow fluctuations in Cases 2–5 are more severe than in Case 1, indicating the implementation of a DR program based on a TOU pricing tariff could decrease the stability of tie-line power. Even if the TOU price-driven DR program is easy to follow, a quantitative understanding of flexibility potential of the DES and end-users’ response to a TOU price tariff should be pursued.

to the customized tie-line power profile. In this case study, the electric load, shown in Fig. 5, is employed as the customized tie-line power profile. The Pareto frontier is shown in Fig. 10(c). When the acceptable deviation ( ) equals zero, the optimal operational cost is the same as in Case 1, which indicates there is no flexibility measure available. It also means DES’s energy flexibility potential is affected by many constraints (i.e., in this paper, the peak power constraint, ramping rate constraint, and acceptable deviation). When the acceptable deviation changes from 0% to 100%, the optimal operational cost decreases linearly. The 100% acceptable deviation is the upper bound to maximize flexibility potential in this case study. As the elastic load was achieved by sacrificing the end-users’ comfort, operational cost, or both, economic compensation should be provided by power grid company. Therefore, it is very important for endusers and power grid company to evaluate the economic value and potential of flexibility measures. In [39], a constant economic coefficient was employed to calculate the elastic load, while, Fig. 10 shows that the constant used to price the flexibility may not be exactly accurate. In addition, there is technical boundary for each ancillary service. The end-user should pay more attention to the lower boundary, as it presents the greatest DR potential. The power grid operator should focus on the upper boundary, as it indicates that the end-users’ economic benefit will not be lost when the ancillary-service objective exceeds the upper boundary.

5.2. The effects of ancillary service on end-users and the power grid As discussed in Section 5.1, the TOU priced-driven DR program could negatively impact the power grid stability, especially when uncontrolled residential users are involved in the DR program [28]. The DES, benefiting from its independent and centralized jurisdiction, can provide controllable ancillary service to the power grid with various of energy flexibility measures. The Pareto frontiers that minimize operational costs (Eq. (1)) and the above ancillary service objectives (Eqs. (25)–(27)) are shown in Fig. 10. All the operational costs decrease with the relaxation of ancillary service objectives (i.e. increasing the allowable maximum tie-lie power limit in AC_PPC, enlarging the range of the tie-line power ramping rate in AC_RRC and allowing greater fluctuation deviation in AC_CPP). Regarding the tie-line’s peak power, Fig. 10(a) shows that the lowest grid limit of the tie-line’s power (P ) is 300 kW, which indicates no additional energy flexibility can be provided by the DES to reduce peak power further. When the allowable peak power is between 300 kW and 1300 kW, the optimal operational cost decreases linearly with the increase in allowable peak power. However, the optimal operational cost will not decrease when the allowable peak power is greater than 1300 kW. This indicates there is a minimum upper bound of the tieline’s peal power, which can maximize flexibility potential to obtain the lowest operational cost. Another ancillary service (AC_RRC) test is conducted with different ramping rate constraints. Similar to AC_PPC, the Pareto frontier is divided into three regions, as shown in Fig. 10(b). The lowest limit of the ramping rate is ± 20 kW/h. When the allowable ramping rate ranges between ± 20 kW/h and ± 1000 kW/h, the optimal operational cost decreases logarithmically with increases in the allowable ramping rate. The upper bound to maximize flexibility potential is ± 1000 kW/h in this case study. For the AC_CPP, the optimal operational cost must be closely related

5.3. Applications of the study The proposed flexibility measure models, economic dispatch model, and ancillary service models are widely applicable to either end-users or power gird operators. First, the economic dispatch results elaborate on how the TOU price drives end-users to adopt flexible measures in the DES to respond to price fluctuation. From the end-user’s perspective, the results obtained from the economic dispatch model may help them to be more informed about the DES’s energy flexibility potential, and, thus, respond optimally to the TOU price to achieve cost reductions. For power gird managers, the economic dispatch model proposed in this paper allows them to better estimate end-users’ electricity consumption behavior. In [48], the authors optimized the customized electricity retail prices while considering end-users’ load features. The data mining techniques were adopted to capture end-users’ load profiles and a 10

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Fig. 10. Pareto frontiers for three ancillary services.

mixed-integer nonlinear model (MINLP) was proposed to optimize the structure of TOU prices and the price level. The results in [48] reveal that customizing TOU prices and integrating them with the end-users’ electricity consumption behavior can better serve the power grid. Therefore, the flexibility measure models and the end-users’ economic dispatch model proposed in this study will assist power grid managers in simulating and predicting end-user loads when established TOUbased DR program. Moreover, the models established in this paper can further assist the power grid managers in testing different electricity retail prices to set more suitable local TOU prices. Although a TOU price-driven DR program is an effective solution to reduce power consumption during peak price periods, it may create a new and much higher peak during off-peak price periods [28]. With the development of multi-energy systems, electricity, heat, natural gas, and other energy carriers are coupled, which makes it possible to achieve an integrated demand response (IDR) [49]. A DES is an integrated energy system in which many kinds of controllable flexibility measures are applied. Therefore, the end-user can achieve peak power reduction based on a TOU price-driven DR program, and their power consumption can be changed flexibly by adjusting the DES’s dispatch strategy,

including allowing power grid managers to customize end-users’ power consumption profiles. In this study, three ancillary service models are proposed to balance the end-user’s operational costs and facilitate interaction with the power grid. The implementation of ancillary services will be a win-win strategy for end-users and power grid managers, and the results of this study can make them more informed about the magnitude and economic value of a DES’s flexibility potential, thereby increasing the likelihood end-users will sign a DR auxiliary service agreement with the power grid and vice versa. The results discussed in Section 5.2 also elaborate on how to price the flexibility resource, which may be a little fuzzy in [39]. Finally, it should be noted that the proposed ancillary services and the optimal dispatch model (MILP model) of the DES are universal and can be applied to other case studies, but the results obtained in this study are based on the specific weather, load and economic conditions, which might not be applicable to other cases. 6. Conclusions This paper focuses on the flexible dispatch of a DES in the presence 11

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of TOU price-driven demand DR program. A DES installed in a Chinese industrial park is used as the case study. First, the flexibility measures are classified into three categories and modeled in detail. Next, a MILP model was proposed to minimize the operating cost of the DES. With the MILP model, the influence of the optimized dispatch strategy on end-users and the power grid are analyzed. Three types of ancillary service models are proposed to facilitate interactions between end-users and the power grid, and the effects of auxiliary services on end-users’ operational costs and ancillary service objectives are analyzed via the multi-objective optimization method. The main findings are as follows:

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(1) A DES can provide a wealth of flexibility resources, and a DR program is an effective way to unlock its energy flexibility. Consequently, the operating cost of the DES can be decreased significantly. (2) Although the TOU price-driven demand response (DR) program can achieve a reduction in end-users’ operating costs, it can also have a negative impact on cooperation between end-users and the power grid. The response characteristics of different flexibility measures vary. The flexibility measure based on energy substitution results in more pronounced power fluctuations in tie-line, but does not create a new peak power. The flexibility measures based on energy storage and buildings’ thermal mass increase both power fluctuations and peak power in the tie-line. Certainly, the flexibility potential of harnessing buildings’ thermal mass is limited, so it has an insignificant impact on tie-line power stability. (3) Auxiliary service is a win–win solution for the end-user and the power grid. Most importantly, multi-objective optimization should be conducted to investigate the maximum flexibility-potential boundary, economy-independent boundary, and the relationship between the ancillary service’s objective and end-user costs. This study is an initial attempt to analyze and discuss the possibility of providing ancillary services to the power grid from the end-user’s perspective. Future work is anticipated to build on the advantages identified in this study. Since the research object of this paper is a single DES, the scheduling strategy may only have a weak impact on the power grid, and the flexibility potential of a DES may be limited for the power grid. In the future, it may be necessary to consider the demand response when multiple DESs are integrated. CRediT authorship contribution statement Jide Niu: Conceptualization, Methodology, Software, Investigation, Writing - original draft, Writing - review & editing. Zhe Tian: Conceptualization, Resources, Supervision. Jie Zhu: Visualization, Validation. Lu Yue: Writing - review & editing, Validation. Declaration of Competing Interest 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. Acknowledgement The work is supported by the National Key Research and Development Program of China (Project No. 2017YFB0903404). References: [1] Energy Technology Perspectives, 2017, [online]. < Available > : https://webstore. iea.org/energy-technology-perspectives-2017. [2] Han J, Ouyang L, Xu Y, Zeng R, Kang S, Zhang G. Current status of distributed energy system in China. Renew Sustain Energy Rev 2016;55:288–97. [3] Di Somma M, Yan B, Bianco N, Graditi G, Luh PB, Mongibello L, et al. Multi-objective design optimization of distributed energy systems through cost and exergy

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