Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers
Abstract
1. Introduction
- (1)
- The existing air-conditioning user demand elasticity model is mostly based on simple regression analysis, ignoring the complex mechanism of economic benefits and thermal comfort on the user’s willingness to participate in the demand response, so it is difficult to accurately quantify the benefit needs of DR control strategies for different air-conditioning users.
- (2)
- Existing demand response regulation methods ignore the differentiated economic benefits and thermal comfort needs of different users, failing to establish a unified multi-objective coordination mechanism. This results in suboptimal outcomes that do not align with user’s dual requirements for economic benefits and thermal comfort, ultimately diminishing user engagement in demand response initiatives.
- (1)
- A comprehensive benefit quantification model for air-conditioning users that takes into account the DR economic benefits and thermal comfort is established, which aims to accurately quantify the benefit of DR control strategies for different types of air-conditioning users.
- (2)
- An optimization method for DR regulation strategies is proposed, with the objective of best comprehensive benefits for air conditioning users, which aims to meet the differentiated benefit needs of different users.
2. Methods
2.1. HVAC Customer’s DR Benefit Mechanism
2.2. Comprehensive Benefit Quantification Model of Air-Conditioning Customers
- (1)
- Comprehensive benefit composition and calculation modelAccording to the demand of office building DR customers, the comprehensive benefit calculation model of air conditioner users can be established. The total benefit is the sum of the comprehensive benefits in the pre-cooling period, response period, and recovery period.The comprehensive benefit of each time step comprises two elements. The calculation formula is as follows:
- (2)
- Economic benefit calculation modelThe economic benefit comprises the electricity economic cost and the DR economic incentive. The calculation formula is as follows:
- (3)
- Comfort equivalent benefit calculation modelIt has been evidenced by research that building infrastructure, sanitary conditions, and thermal environment have significant impacts on employee performance, which subsequently influences the economic benefits of enterprises [34]. The change in equivalent economic benefits caused by the change in the user’s thermal comfort during the DR period is defined as the difference between the benefit at a certain time in the process of pre-cooling, response, and recovery, and the benefit at the baseline temperature, which is calculated as follows:The work performance percentage KPI here is affected by the environmental thermal comfort. According to GB/T 50785-2012 “Civil Thermal and Humid Environment Evaluation Standard” [36], the Predicted Mean Vote (PMV) is used to characterize the comfort of the indoor environment. The typical model in the existing research is selected to describe the influence of the PMV on employees’ work performance [37], and the calculation expression of KPI is as follows:
2.3. DR Strategy Optimization Model of Air-Conditioning Customers
2.3.1. Demand Response Implementation Model
2.3.2. User-Side Demand Response Strategy Optimization Process
2.4. Effectiveness Validation Method of the Optimized DR Strategy: A Case Study
2.4.1. Boundary Condition of Building Case
2.4.2. Air-Conditioning Customers’ Benefit Parameters Setting
2.4.3. Control Strategy Setting
- (1)
- Grid-side power curtailmentFor the predefined 1 h demand response period with 15 min timesteps, power curtailment is defined as the average difference between baseline power and response power across four timesteps (t = 2 to 5). Greater curtailment indicates greater alignment with grid-side objectives. The power curtailment for an individual user is calculated according to Formula (16).
- (2)
- User-side benefitsUser benefits are evaluated across six timesteps encompassing pre-cooling, response, and recovery periods. Higher benefits reflect greater user-friendliness of the strategy. The benefits are quantified according to Formulae (17)–(19):
2.4.4. Simulation Verification Process
3. Results
3.1. Optimized DR Temperature Setpoints and Load Sequences
- (1)
- Analysis of optimal temperature setpointsDuring the pre-cooling period, most users adopted a strategy of lowering the temperature setpoint to its minimum value. In the response period, significant inter-user differences emerged:
- (a)
- Customers with smaller labor elasticity coefficients (α) and lower baseline profitability (W) (customers 1, 2, 4, 5, 7, 10, and 13) maintained a constant elevated setpoint of 28 °C.
- (b)
- Users with larger α and higher W (customers 3, 6, 8, 9, 11, 12, 14, and 15) exhibited fluctuating temperature adjustments, characterized by alternating increases and decreases in setpoints during the response period. During the recovery period, most users reduced their temperature setpoints to a low level.
- (2)
- Analysis of optimal load sequencesThe pre-cooling phase led to an increase in load due to the reduced temperature settings. In the response period, the following occurred:
- (a)
- Customers with smaller α and W (customers 1, 2, 4, 5, 7, 10, and 13) achieved stable load reductions.
- (b)
- Customers with larger α and W (customers 3, 6, 8, 9, 11, 12, 14, and 15) displayed intermittent load fluctuations, with temporary load increases corresponding to temperature adjustments. During the recovery period, load levels rose as users readjusted setpoints downward.
3.2. Effectiveness Analysis of Dynamic Optimal DR Strategy
- (1)
- Effectiveness of the dynamic optimal strategy for individual customersUsing customer 8 as a representative case, Figure 9 compares the performance of three control strategies: optimized strategy (orange, dynamic optimal temperature adjustments), traditional strategy A (blue, fixed 1 °C temperature increase), and traditional strategy B (green, Fixed 2 °C temperature increase). Figure 9a illustrates load reduction and temperature setpoints, with vertical color blocks demarcating pre-cooling, response, and recovery periods. Figure 9b quantifies economic benefits (upward bars), comfort loss (downward bars), and comprehensive benefits (area plots).At time steps 1 and 3, the optimized strategy lowered the setpoint temperature, increasing load reduction. Subsequent adjustments at time steps 2 and 4 raised setpoints from lower baseline values, further enhancing economic benefits without significantly compromising comfort. This demonstrates that the optimized strategy leverages the building envelope’s thermal inertia to balance economic gains and comfort preservation. Across all periods, the optimized strategy achieved higher average load reduction and greater comprehensive benefits than traditional strategies, confirming its ability to maximize user-specific trade-offs between economic and comfort benefits.
- (2)
- Effectiveness of dynamic optimal strategy at the population levelFigure 10 quantifies the optimized strategy’s superiority over traditional methods at the population level using the four metrics defined in Section 2.4.3:
- (a)
- Load ReductionAs illustrated in Figure 10a, during the response period, the load reduction effect of all samples was superior to strategy A. And the load reduction effect of 61% of samples demonstrated a higher reduction than that of strategy B.
- (b)
- Economic BenefitsAs illustrated in Figure 10b, all air-conditioning users exhibited a greater economic benefit than the baseline.
- (c)
- Comfort LossAs illustrated in Figure 10c, the loss of comfort benefits for the 50% of consumers in the sample cluster was less than that of the two traditional strategies. And the loss of comfort benefits for the remaining 50% of consumers was less than that of strategy B and more than that of strategy A.
- (d)
- Comprehensive BenefitsAs illustrated in Figure 10d, the comprehensive benefit of the entire sample population was superior to the two traditional strategies A and B.
3.3. Characteristic Difference Analysis of Culture Customers
4. Discussion
5. Conclusions
- (1)
- Compared with the two traditional flexible strategies, the optimization strategy effectively improves the load reduction effect and comprehensive benefits. From the perspective of load reduction effect, the load reduction effect of all HVAC users adopting the optimization strategy is higher than that of traditional strategy A (indoor temperature control point increased by 1 °C), and the load reduction effect of 61% of users is higher than that of traditional strategy B (indoor temperature control point increased by 2 °C). From the perspective of comprehensive benefits, the comprehensive benefits of all users adopting the optimization strategy are higher than those of the two traditional strategies.
- (2)
- The optimization strategy effectively improves the thermal comfort of HVAC users. In total, 50% of customers adopting the optimization strategy have less comfort benefit loss than the two traditional strategies, and the remaining 50% are between the two traditional flexible strategies.
- (3)
- HVAC users with lower basic profitability and a lower labor elasticity coefficient are better targets for air-conditioning load demand response. Users with lower basic profitability and a lower labor elasticity coefficient have higher load reduction potential and greater comprehensive benefit. In the application scenario involving the optimal scheduling of DR customer clusters, this type of customer should have a higher priority.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Phase | DR Incentives | Economic Benefit | Comfort Equivalent Benefit | Profit Function |
---|---|---|---|---|
Response period | ✓ | Generating economic benefit through load reduction (+) | Operational efficiency losses (−) | Economic benefit + Operational efficiency losses |
Pre-response and post-response | ✗ | Increases energy consumption and electricity costs (−) | Operational efficiency increase (+) | Electricity costs + Operational efficiency increase |
Customer Characteristics | W1 = 10,000 CNY/h | W2 = 20,000 CNY/h | W3 = 30,000 CNY/h | W4 = 40,000 CNY/h | W5 = 50,000 CNY/h |
---|---|---|---|---|---|
α1 = 0.5 | Customer 1 | Customer 4 | Customer 7 | Customer 10 | Customer 13 |
α2 = 1 | Customer 2 | Customer 5 | Customer 8 | Customer 11 | Customer 14 |
α3 = 2 | Customer 3 | Customer 6 | Customer 9 | Customer 12 | Customer 15 |
Control Strategies | Stages | |||
---|---|---|---|---|
Pre-Cooling | Response | Recovery | ||
Optimization Strategies | Optimization of the Room Temperature Setting Within the Adjustable Range | |||
Conventional strategies | Strategy A | No pre-cooling | Indoor temperature set point up to 27 °C | Indoor temperature set point down to baseline 26 °C |
Strategy B | No pre-cooling | Indoor temperature set point up to 28 °C | Indoor temperature set point down to baseline 26 °C |
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Li, B.; Wang, Y.; Jiang, H.; Wang, R.; Lu, S. Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers. Buildings 2025, 15, 1065. https://doi.org/10.3390/buildings15071065
Li B, Wang Y, Jiang H, Wang R, Lu S. Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers. Buildings. 2025; 15(7):1065. https://doi.org/10.3390/buildings15071065
Chicago/Turabian StyleLi, Boyang, Yuhan Wang, Houze Jiang, Ran Wang, and Shilei Lu. 2025. "Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers" Buildings 15, no. 7: 1065. https://doi.org/10.3390/buildings15071065
APA StyleLi, B., Wang, Y., Jiang, H., Wang, R., & Lu, S. (2025). Demand Response Strategy Optimization Method Based on Differentiated Comprehensive Benefit Model of Air-Conditioning Customers. Buildings, 15(7), 1065. https://doi.org/10.3390/buildings15071065