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

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## Abstract

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## 1. Introduction

#### 1.1. Background and Motivation

#### 1.2. Novelty and Contribution

- (1)
- The interruptible power load, shiftable power load, and adjustable thermal load are modeled, respectively, and are optimized by UDDSR scheme in order to obtain the aggregated IDR bids.
- (2)
- An aggregated buildings thermal model is introduced to measure the temperature requirements of the entire community of users for heating. The adjustable thermal loads of the IDR bids submitted by users are modeled within the context of air temperature, and can be optimized by regulating the indoor temperature of users.
- (3)
- From the overall perspective of system operation, a day-ahead scheduling optimization model for the community IES based on UDDSR is established, and the CVaR theory is introduced to deal with the uncertainties in IES.

## 2. Demand Response Load Modeling Based on UDDSR

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

_{in}

_{min}/T

_{in}

_{max}is the minimum/maximum indoor temperature that costumers are willing to accept, respectively; T

_{l,i}/T

_{u,i}is the minimum/maximum heating temperature submitted by the user i.

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

_{air}is the air specific heat; ${L}_{AC}^{t}$ is the adjustable thermal load at time t; ${T}_{in}^{t}$ and ${T}_{out}^{t}$ are the indoor and outdoor temperature at time t.

_{adj}is the maximum adjustable indoor temperature allowed by end users during IDR event; ${T}_{DRH}^{t}$, determined by IDR bids, is the adjustable time of thermal load allowed by users, and if ${T}_{DRH}^{t}$ = 1/${T}_{DRH}^{t}$ = 0, the thermal load can/cannot be adjusted; ΔT

_{max}is the maximum indoor temperature variation during Δt, 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

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

#### 3.1. PV Model

_{stc}is the maximum PV output power under standard test conditions; G

^{t}is the light intensity and G

_{stc}is that under standard test conditions; ε is the PV power temperature coefficient; ${T}_{s}^{t}$ is surface temperature of PV and T

_{stc}is that under standard test conditions.

#### 3.2. Power Supply Equipment Model

#### 3.2.1. Microgas Turbine (MT) Model

_{ng}is the calorific value of natural gas; η

_{MT}is the MT power generation efficiency; ${Q}_{MT}^{t}$ is the MT output heat power at time t; η

_{loss}is the MT power loss efficiency.

#### 3.2.2. Gas Boiler (GB) Model

_{GB}is the GB heat production efficiency.

#### 3.2.3. Waste Heat Recovery (WHR) Device Model

_{WHR}is the WHR heat recovery efficiency.

#### 3.2.4. Heat Exchanger (HE) Model

_{HE}is the HE heat exchange efficiency.

#### 3.3. Energy Storage Equipment Model

#### 3.3.1. Battery (BT) Model

_{BT,loss}is the power loss rate of BT; ${P}_{BT,ch}^{t}$ and ${P}_{BT,dis}^{t}$ are the charging and discharging power of BT, respectively; η

_{BT,ch}and η

_{BT,dis}are the charging and discharging efficiency of BT, respectively.

#### 3.3.2. Thermal Storage Tank (TST) Model

_{TST,loss}is the energy loss rate of TST; ${Q}_{TST,ch}^{t}$ is the heat storage power of TST; η

_{TST,ch}is the heat storage efficiency; ${Q}_{TST,dis}^{t}$ is the heat release power; η

_{TST,dis}is the heat release efficiency.

## 4. Community CHP System Model Based on UDDSR

#### 4.1. Day-Ahead Energy Optimization Model

_{total}is the total cost of system operation; T

_{ref}is the national standard indoor optimum temperature; δ

_{t}is a time-varying parameter that measure the thermal comfort of users, and during the UDDSR event, δ

_{t}is relaxed to achieve the purpose of temperature regulation and consumption reduction, while in other moments δ

_{t}plays the role of making the indoor temperature close to the optimal temperature; N is the optimal scheduling cycle.

_{grid}is the cost of electricity purchasing from the grid; C

_{ng}is the cost of natural gas; C

_{om}is the cost of equipment operation and maintenance; C

_{UDDSR}is the total subsidy for UDDSR participation given to users by the operator.

_{grid}and C

_{ng}can be calculated as:

_{gas}is the price of natural gas.

_{om}can be calculated as:

_{om,MT}, C

_{om,GB}and C

_{om,PV}are the unit power operation and maintenance costs of MT, GB, and PV, respectively.

_{UDDSR}can be calculated as:

_{u}is the load response subsidy for users; C

_{es}is the energy storage subsidy; ${e}_{DRE}^{t}$ is electric load response compensation per unit power; ${e}_{DRH}^{t}$ is the thermal load response compensation per unit power; ${L}_{DRH}^{t}$ is the change of thermal power caused by lowering the room temperature ΔT

^{t}within the range allowed by users at time t; ${T}_{in0}^{t}$ is the indoor temperature before UDDSR event; e

_{BT}is the unit power subsidy for the charging and discharging behavior of BT; e

_{TST}is the unit power subsidy for heat storage and release behavior of TST.

- Energy balancing constraints

- 2.
- Energy supply constraints

_{grid}

_{max}is the maximum interactive power between the community system and the power grid per unit time; P

_{MT}

_{max}and P

_{MT}

_{min}are the maximum and minimum generating power of MT; Q

_{GB}

_{max}is the maximum heating power of GB.

- 3.
- Energy storage constraints

_{BT,ch}

_{max}and P

_{BT,dis}

_{max}are the maximum charging and discharging power of BT; W

_{BT}

_{max}and W

_{BT}

_{min}are the maximum and minimum energy storage capacity of BT.

_{TST,ch}

_{max}and P

_{TST,dis}

_{max}are the maximum heat storing and releasing power of TST; W

_{TST}

_{max}and W

_{TST}

_{min}are the maximum and minimum heat storage capacity of TST.

#### 4.2. CVaR-Based Energy Optimization Model

#### 4.2.1. CVaR Model

_{con}is the average excess loss under a given confidence level; con is the confidence level; f(X, γ) is the loss function; X is the investment portfolio; γ is the risk variable; VaR

_{con}is the expected maximum loss under the con; E[.] expresses the expect function.

_{con}is the minimum value of g(X, α); α is the intermediate variable after relaxation of VaR, and when g(X, α) goes to the minimum, α is equal to VaR

_{con}.

#### 4.2.2. Day-Ahead Energy Optimization Model Based on CVaR

_{forecast}

_{,}σ

^{2}), and the probability distribution formula is:

_{forecast}is the forecast value of r.

_{total,i}is the total cost of system operation in scenario i; E[C

_{total,i}] is the expected cost of system operation in all simulated uncertainty scenarios; ${\varphi}_{i}$ is the middle variable in scenario i; M is the total number of uncertainty scenarios.

_{punish}is the penalty fee when users do not respond according to the response load optimized by day-ahead UDDSR; ${L}_{DRE}^{t}$ is the total actual response load; ${L}_{DRE0}^{t}$ is the response load optimized by day-ahead UDDSR.

## 5. Case Study

#### 5.1. Day-Ahead Energy Optimization Based on UDDSR

#### 5.1.1. Energy Optimization Results without UDDSR Response

#### 5.1.2. Energy Optimization Results with UDDSR Response

_{adj}, i.e., the heating range is changed into T

_{in}

_{min}- T

_{adj}≤ Tt in ≤ T

_{in}

_{max}- T

_{adj}; when L_DRH State < 0, the heating temperature cannot be reduced, i.e., the thermal load cannot be adjusted. In this case, it is assumed that T

_{adj}= 1, T

_{in}

_{min}= 18, T

_{in}

_{max}= 26. It can be seen that the operating costs of the community CHP system are lower when users perform UDDSR based on the optimized IDR response load, compared with performing UDDSR according to the maximum response capacity of the aggregated IDR bid.

_{in}

_{min}- T

_{adj}. This means the community system does not operate according to the minimum heating temperature, which guarantees the energy satisfaction of users to the greatest extent, and verifies the accuracy and validity of the heating temperature constraint in (18)

#### 5.2. CVaR-Based Energy Optimization

#### 5.2.1. Energy Risk Optimization Results Based on CVaR

#### 5.2.2. Impact of Confidence Level and Uncertainty Coefficient of CVaR on Energy Use Optimization

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

Time period | Electricity Price (RMB/kWh) | Power Load Response Subsidy (RMB/kWh) | Thermal Load Response Subsidy (RMB/kWh) | Imbalance Response Penalty (RMB/kWh) | Gas Price (RMB/m³) |
---|---|---|---|---|---|

Peak time ((09:00–13:00], [17:00–20:00)) | 1.19 | 0.3 | 0.2 | 0.6 | 3 |

Normal time ((06:00–08:00], [14:00–16:00)) | 0.75 | 0.1 | 0.2 | 0.4 | 3 |

Valley time ((00:00–05:00], [21:00–23:00)) | 0.36 | 0.05 | 0.2 | 0.18 | 3 |

Parameter | Value | Parameter | Value |
---|---|---|---|

MT generating efficiency | 0.36 | TST heat releasing efficiency | 0.95 |

MT maximum output power | 500 kW | TST self-loss rate of thermal energy | 0.04 |

MT minimum output power | 10 kW | TST maximum capacity | 100 kWh |

GB heat production efficiency | 0.85 | TST minimum capacity | 0 kWh |

GB maximum thermal output power | 600 kW | TST maximum heat storage/release power | 50 kW |

GB minimum thermal output power | 0 kW | Maximum power purchased from the grid | 1000 kW |

BT charging efficiency | 0.95 | Minimum power purchased from the grid | 0 kW |

BT discharging efficiency | 0.95 | Maximum power of interruptible power load | ${L}_{DRE,\mathrm{intmax}}^{t}$ |

BT self-loss rate of electrical energy | 0.04 | Maximum power of shiftable power load | ${L}_{DRE,shf,out\mathrm{max}}^{t}$/${L}_{DRE,shf,in\mathrm{max}}^{t}$ |

BT maximum capacity | 100 kWh | Maximum indoor temperature | 26 |

BT minimum capacity | 0 kWh | Minimum indoor temperature | 18 |

BT maximum charging/discharging power | 50 kW | Optimum indoor temperature | 21 |

TST heat storing efficiency | 0.95 | Maximum adjustable temperature | T_{adj} |

Equipment | Operation and Maintenance Cost (RMB/kWh) | Equipment | Subsidy (RMB/kWh) |
---|---|---|---|

MT | 0.075 | BT | 0.01 |

GB | 0.08 | TST | 0.01 |

PV | 0.01 |

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**Figure 4.**The forecast curves of electric load, hot water load, PV output and outdoor temperature of the system on a typical winter day.

**Figure 5.**Energy optimization results without UDDSR response. (

**a**) Optimization results of electric bus; (

**b**) optimization results of heat bus.

**Figure 8.**Energy optimization results with UDDSR response. (

**a**) Optimization results of electric bus; (

**b**) optimization results of heat bus.

**Figure 9.**Comparison of electric and heat load before and after UDDSR. (

**a**) Comparison of electric load before and after response; (

**b**) comparison of heat load before and after response.

**Figure 12.**System energy optimization results with maximum uncertainty fluctuation ≤10%. (

**a**) Optimization results of electric bus; (

**b**) optimization results of heat bus.

**Figure 13.**Comparison of indoor heating temperature in different scenarios (con = 0.95, β = 1): (

**a**) maximum uncertainty fluctuation ≤5%; (

**b**) maximum uncertainty fluctuation ≤10%; (

**c**) maximum uncertainty fluctuation ≤15%; (

**d**) maximum uncertainty fluctuation >15% (about 50%).

**Figure 14.**The changes of expected cost of system operation with con and β (maximum uncertainty fluctuation ≤10%).

Before UDDSR | After UDDSR | Saving (%) | |
---|---|---|---|

Electricity purchasing cost (RMB) | 7344.90 | 6715.64 | 8.57% |

Gas purchasing cost (RMB) | 9153.34 | 9001.82 | 1.66% |

Operation and maintenance (RMB) | 1122.07 | 1108.60 | 1.20% |

Power load response compensation (RMB) | 0 | 205 | / |

Thermal load response compensation (RMB) | 0 | 45.49 | / |

BT subsidies (RMB) | 0 | 5.70 | |

Adjustable temperature (°C) | 0 | 1 | / |

Total cost (RMB) | 17,620.31 | 17,076.56 | 3.09% |

Scenarios | Before UDDSR | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Maximum risk fluctuation | 0% | ≤5% | ≤10% | ≤15% | >15% |

Electricity purchasing cost (RMB) | 7344.90 | 6889.18 | 7293.46 | 8048.00 | 11529.32 |

Gas purchasing cost (RMB) | 9153.34 | 9208.27 | 9177.42 | 9338.93 | 9765.28 |

Power load response subsidies (RMB) | 0 | 203.25 | 200.87 | 196.98 | 183.88 |

Thermal load response subsidies (RMB) | 0 | 37.32 | 45.13 | 26.54 | 20.97 |

BT subsidies (RMB) | 0 | 5.70 | 5.70 | 5.71 | 5.93 |

Imbalance response penalty (yaun) | 0 | 3.77 | 8.81 | 17.21 | 46.23 |

Adjustable tmperature (°C) | 0 | 1 | 1 | 1 | 1 |

Total expected cost of operation (RMB) | 17,620.31 | 17,461.61 | 17,832.64 | 18,739.14 | 22,662.36 |

CVaR (RMB) | 0 | 4.13 | 5.21 | 17.45 | 41.01 |

Total cost savings ratio | / | 0.90% | −1.21% | −6.34% | −28.61% |

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## Share and Cite

**MDPI and ACS Style**

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

**AMA Style**

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(15):4398.
https://doi.org/10.3390/en14154398

**Chicago/Turabian Style**

Li, Yiqi, Jing Zhang, Zhoujun Ma, Yang Peng, and Shuwen Zhao.
2021. "An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response" *Energies* 14, no. 15: 4398.
https://doi.org/10.3390/en14154398