*Article* **An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response**

**Yiqi Li <sup>1</sup> , Jing Zhang 1,\*, Zhoujun Ma 2,3, Yang Peng <sup>1</sup> and Shuwen Zhao <sup>1</sup>**


**Abstract:** With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers' willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users' willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

**Keywords:** community integrated energy system; energy management; user dominated demand side response; conditional value-at-risk

#### **1. Introduction**

#### *1.1. Background and Motivation*

The development of energy cogeneration and integration technologies as well as renewable energies (e.g., photovoltaic (PV)) has attracted many scholars to undertake research on integrated energy systems (IES). The term IES takes into consideration many kinds of energy subsystems, e.g., electricity supply, gas supply, heating, cooling [1,2]. Different forms of energy are coupled and closely connected through energy conversion equipment (e.g., combined heat and power (CHP) unit, electric heating equipment), and can meet the diverse energy demands of users. However, because of its multienergy coupling characteristic, it is impossible to design, plan and optimize separately the operation of various energy supply systems as the traditional distributed energy supply system does [3]. Therefore, how to efficiently deal with the complementarity and substitution between different energy streams has become a key issue to realize energy cascade utilization and to improve comprehensive energy utilization efficiency. Additionally, the traditional energy management system (EMS) framework cannot adapt to the coexistence and interaction features of centralization and distribution in IES [4,5] (e.g., the energy management policy

**Citation:** Li, Y.; Zhang, J.; Ma, Z.; Peng, Y.; Zhao, S. An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response. *Energies* **2021**, *14*, 4398. https://doi.org/ 10.3390/en14154398

Academic Editors: Leijiao Ge, Jun Yan and Yonghui Sun

Received: 23 June 2021 Accepted: 19 July 2021 Published: 21 July 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

proposed in [6] only considers the electric energy, and cannot be applied to deal directly with multienergy flow problems). Therefore, it is necessary to study the integrated energy management system (IEMS) technology for multienergy flow.

The control objects of the IEMS can be divided into three layers. The upper layer is the system-level multienergy flow transmission network, which involves the production, transmission and safe operation of energy such as gas, electricity, and heat. The middle layer is a local microenergy unit, with industrial parks, smart communities, and intelligent buildings as typical application scenarios, and it involves the coordinated scheduling and optimized operation of multiple energy sources. The lower layer is the user-level integrated producer and consumer. The multienergy complementarity and alternative features of IEMS not only provide users with more options for energy use, but also bring optimization space for the overall regulation and operation of the system. With the development of IES, existing studies based on the traditional demand response (DR) technologies for a single energy source (electric energy) [7–9] can no longer meet users' actual needs, and there is an urgent need to study integrated demand response (IDR) technology for integrated energy. Reasonable use of user-side responsive resources to participate in the IDR of the system will play an important role in realizing the two-way interaction between the supply and the demand and the win–win situation [10]. On the other hand, information communication and engineering measurement and control technology have developed rapidly recently. Having access to a large number of smart sensors has greatly increased the amount of multienergy flow information that can be collected by the middle layer and user layer of IEMS. The IEMS can adjust in time based on the measurement or user feedback information, and improve energy efficiency and operating economy on the premise of ensuring the user's energy comfort.

Thus far, the IDR strategies and mechanisms have been studied for many purposes. In [11], the concept of IDR was first proposed and gas turbines were introduced to supply power to the power grid during peak time, converting part of the power load into gas load. Additionally, the incentive effect of natural gas prices on IDR was analyzed through Nash game theory. In [12], the physical constraints of the natural gas network and the heating network were processed by piecewise linearization, and an IDR optimal transaction strategy model based on the mixed-integer second-order cone programming algorithm and transaction price incentive was proposed. The authors in [13] summarized the development of IDR from the aspects of system modeling, optimization strategy and power market mechanisms, and affirmed the positive effect of IDR on improving the flexibility of IES load response. In [14], an IDR model based on medium- and long-term time dimensions considering system dynamics was proposed, and taking flexible loads, energy storage, and electric vehicles into account, an IES scheduling model was established in order to simulate the benefits for users participating in IDR. In [15], a day-ahead and intraday optimization scheduling model based on the demand side response was proposed, and the scheduling times for different energy subsystems were considered to perform rolling optimization scheduling.

Current research mostly focuses on the impact of market price mechanisms and the refined modeling of IES equipment and networks on IDR [16,17]. It is assumed that users will continue to participate in IDR events satisfactorily under certain price incentives, or users will maximize their responsive load during IDR events. Additionally, users are assumed to allow their own load equipment to be adjusted by EMS or energy service providers. However, most research ignored users' willingness to participate in DR programs. In fact, users are not necessarily willing to give the control of the equipment to EMS or energy service providers underprice incentives [18]. Users may not provide the maximum responsive load during IDR due to privacy reasons. In [18], a survey was conducted on the willingness of 1499 households from a state in Australia to participate in a direct load control (DLC) plan, and the results showed that only about 13% of customers accepted the DLC plan. For users, the main reason for reluctance to participate in the DLC program is that users have low trust in energy companies. At present, there are very few studies on the

relationship between user satisfaction with participating in IDR events and response load capacity. In [19], a user dominated demand side response (UDDSR) scheme that allows energy users to dynamically choose to join or withdraw from DR events was put forward. In this scheme, users can submit flexible DR bids to community EMS for participating in DR events. That is, users can flexibly choose the working hours of each household device. However, this scheme only focuses on electric load, and fails to consider the overall optimization within IES.

#### *1.2. Novelty and Contribution*

In this paper, an energy management optimization method for community IES based on UDDSR is put forth, where users can submit the day-ahead IDR bid for load responses that fully meets their own comfort, and respond to the IDR requests issued by the power grid operator or dispatching department according to the planned capacity of the IDR bid on the next day. Additionally, an aggregated buildings thermal model is introduced to establishe the adjustable thermal load model, and the user's power load adjustable time, power load adjustable capacity, thermal load adjustable time and heating temperature are set as optimized parameters to establish a day-ahead scheduling model. Considering the uncertainty of PV output, user load, outdoor temperature, and user actual UDDSR response capacity in the community IES, a penalty mechanism is introduced to punish the participants making imbalanced response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness under different prediction accuracy.

The contributions of this paper are summarized as follows:


#### **2. Demand Response Load Modeling Based on UDDSR**

In this paper, the detailed UDDSR optimization approach is based on the mechanism described in [19]. This mechanism allows users to submit flexible bids for DR events and achieves the optimal aggregation of these bids within the DR events. However, it only considers electric equipment including interruptible appliances (e.g., heating systems) and shiftable appliances (e.g., electric vehicles). In this section, the UDDSR optimization with adjustable thermal loads is further studied within the IDR events.

#### *2.1. UDDSR Optimization with Adjustable Thermal Loads*

In this paper, thermal loads of the aggregated buildings are modeled within the context of air temperature, and can be adjusted by regulating the indoor temperature of end users. Regarding the adjustable thermal loads of the IDR bids, the maximum and minimum of the heating temperature, the maximum adjustable temperature for heating, and the adjustable time period for heating can be set by users. Since this paper studies the centralized temperature regulation in the case of central heating, the community energy management system (CEMS) will first classify users according to the maximum adjustable temperature for heating in the IDR bids. For users who have the same adjustable temperature, CEMS will select as many users as possible who are willing to adjust the heating temperature within the IDR request period to participate in the UDDSR thermal load response according to (1). Additionally, CEMS will select the minimum of the highest

temperatures and the maximum of the lowest temperatures submitted by all users in the IDR bids as the temperature constraint range of the central heating, as demonstrated in (2). peratures and the maximum of the lowest temperatures submitted by all users in the IDR bids as the temperature constraint range of the central heating, as demonstrated in (2).

*N*

temperature for heating in the IDR bids. For users who have the same adjustable temperature, CEMS will select as many users as possible who are willing to adjust the heating temperature within the IDR request period to participate in the UDDSR thermal load response according to (1). Additionally, CEMS will select the minimum of the highest tem-

$$\min\_{t \in T} VAR(M\_u^t) \tag{1}$$

$$\begin{cases} \begin{array}{c} T\_{\text{irmm}} = \max\limits\_{i=1}^{N\_{\text{ul}}} \{ T\_{l,i} \} \\ \begin{array}{c} T\_{\text{ilmax}} = \min\limits\_{i=1}^{N\_{\text{ul}}} \{ T\_{u,i} \} \end{array} \end{cases} \tag{2}$$

where *M<sup>t</sup> u* is the total number of users willing to participate in UDDSR thermal load response at time *t*; *Tin*min/*Tin*max is the minimum/maximum indoor temperature that costumers are willing to accept, respectively; *Tl*,*i*/*Tu*,*<sup>i</sup>* is the minimum/maximum heating temperature submitted by the user *i*. where *M<sup>t</sup> <sup>u</sup>* is the total number of users willing to participate in UDDSR thermal load response at time *t*; *Tin*min/*Tin*max is the minimum/maximum indoor temperature that costumers are willing to accept, respectively; *Tl,i* /*Tu,i* is the minimum/maximum heating temperature submitted by the user *i*.

#### *2.2. Adjustable Thermal Loads Model Based on UDDSR 2.2. Adjustable Thermal Loads Model based on UDDSR*

*Energies* **2021**, *14*, x FOR PEER REVIEW 4 of 24

According to [20], the thermodynamic model of the aggregated buildings can be formulated as the RC equivalent circuit model, as demonstrated in Figure 1, where *R* is the equivalent thermal resistance of the house shell; *Cair* is the air specific heat; *L t AC* is the adjustable thermal load at time *t*; *T t in* and *T t out* are the indoor and outdoor temperature at time *t*. According to [20], the thermodynamic model of the aggregated buildings can be formulated as the RC equivalent circuit model, as demonstrated in Figure 1, where *R* is the equivalent thermal resistance of the house shell; *Cair* is the air specific heat; *Lt AC* is the adjustable thermal load at time *t*; *Tt in* and *Tt out* are the indoor and outdoor temperature at time *t*.

**Figure 1.** Thermodynamic model of the aggregated buildings. **Figure 1.** Thermodynamic model of the aggregated buildings.

Therefore, the relation equation between indoor temperature and adjustable thermal load is as follows: Therefore, the relation equation between indoor temperature and adjustable thermal load is as follows:

$$\frac{dT\_{in}^t}{dt} = -\frac{1}{\mathcal{R} \cdot \mathbb{C}\_{air}} \cdot T\_{in}^t + \frac{1}{\mathbb{C}\_{air}} \cdot \left(L\_{AC}^t + \frac{1}{\mathcal{R}} \cdot T\_{out}^t\right) \tag{3}$$

The discrete model of (3) is The discrete model of (3) is

Then, the adjustable thermal load *Lt* 

$$T\_{\rm in}^t = T\_{\rm in}^{t-\Delta t} \cdot e^{-\frac{\Delta t}{\mathcal{R} \cdot \mathcal{C}\_{\rm air}}} + \left(\mathcal{R} \cdot L\_{\rm AC}^t + T\_{out}^t\right) \cdot \left(1 - e^{-\frac{\Delta t}{\mathcal{R} \cdot \mathcal{C}\_{\rm air}}}\right) \tag{4}$$

*AC* is calculated from:

*R C*

where *e* is a constant; Δ*t* is the scheduling interval and is assumed to be 1 h in this paper. where *e* is a constant; ∆*t* is the scheduling interval and is assumed to be 1 h in this paper. Then, the adjustable thermal load *L t AC* is calculated from:

$$L\_{\rm AC}^{t} = \frac{1}{R} \cdot \left(\frac{T\_{in}^{t} - T\_{in}^{t-\Delta t} \cdot e^{-\frac{\Delta t}{R \cdot C\_{air}}}}{1 - e^{-\frac{\Delta t}{R \cdot C\_{air}}}} - T\_{out}^{t}\right) \tag{5}$$

$$\begin{cases} \left| \begin{array}{l} T\_{\text{immin}} - T\_{\text{adj}} \cdot T\_{\text{DRH}}^{t} \leq T\_{\text{in}}^{t} \leq T\_{\text{immax}} - T\_{\text{adj}} \cdot T\_{\text{DRH}}^{t} \\ \left| T\_{\text{in}}^{t} - T\_{\text{in}}^{t-\Delta t} \right| \leq \Delta T\_{\text{max}} \\ T\_{\text{immin}} \cdot T\_{\text{in}\text{max}} \wedge\_{\text{adj}} \geq 0 \end{array} \tag{6}$$

where *Tadj* is the maximum adjustable indoor temperature allowed by end users during IDR event; *T t DRH*, determined by IDR bids, is the adjustable time of thermal load allowed by users, and if *T t DRH* = 1/*T t DRH* = 0, the thermal load can/cannot be adjusted; ∆*Tmax* is the maximum indoor temperature variation during ∆*t*, and it should be less than 2 ◦C in order not to affect the comfort of users.

#### *2.3. Electric Loads Model Based on UDDSR*

In the community CHP system, the electric loads includes interruptible power loads and shiftable power loads. Based on the aggregated IDR bids obtained from UDDSR optimization in [19], the total response power of the interruptible appliances during the IDR event should be less than the maximum interruptible power at the same time after the aggregated IDR bid. Thus the interruptible power load is expressed as

$$0 \le L\_{DRE, int}^t \le L\_{DRE, int\max}^t \tag{7}$$

where *L t DRE*,*int* is the interruptible power load at time *t*; *L t DRE*,*int*max is the maximum interruptible power load at time *t*, which can be obtained from aggregated IDR bid of end users.

The shiftable load model is expressed as

$$L^t\_{\rm DRE,shf} = L^t\_{\rm DRE,shf,out} - L^t\_{\rm DRE,shf,in} \tag{8}$$

$$\sum\_{t=1}^{T} L\_{DRE, shf, \rho ut}^{t} = \sum\_{t=1}^{T} \left| L\_{DRE, shf, in}^{t} \right| \tag{9}$$

$$\begin{cases} \text{ } 0 \le \mathcal{L}\_{DRE, shf,out}^{t} \le \mathcal{L}\_{DRE, shf,out \text{max}}^{t} \\\ \mathcal{L}\_{DRE, shf,in \text{max}}^{t} \le \mathcal{L}\_{DRE, shf,in}^{t} \le \mathcal{0} \end{cases} \tag{10}$$

where *L t DRE*,*sh f* is the total shiftable power load at time *t*; *L t DRE*,*sh f* ,*out* and *L t DRE*,*sh f* ,*out*max are the load and the maximum load shifted from time *t* to other time; *L t DRE*,*sh f* ,*in* and *L t DRE*,*sh f* ,*in*max are the load and maximum load shifted to time *t*, respectively; *T* is the optimized scheduling cycle; *L t DRE*,*sh f* ,*out*max and *L t DRE*,*sh f* ,*in*max can be obtained from aggregated IDR bid of end users.

#### **3. Distributed Generator and Co-Supply Equipment Model**

#### *3.1. PV Model*

PV is a common distributed generation device in the community, and can be modeled as:

$$P\_{PV}^{t} = P\_{stc} \cdot \frac{G^t}{G\_{stc}} \cdot \left(1 + \varepsilon (T\_s^t - T\_{stc})\right) \tag{11}$$

where *P t PV* is the PV output power; *Pstc* is the maximum PV output power under standard test conditions; *G t* is the light intensity and *Gstc* is that under standard test conditions; *ε* is the PV power temperature coefficient; *T t s* is surface temperature of PV and *Tstc* is that under standard test conditions.
