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Article

Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm

by
Biao Qiao
1,2,*,
Jiankai Dong
1,
Wei Xu
2,
Ji Li
2 and
Fei Lu
2
1
School of Architecture and Design, Harbin Institute of Technology, Harbin 150080, China
2
China Academy of Building Research, Beijing 100013, China
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(5), 836; https://doi.org/10.3390/buildings15050836
Submission received: 24 December 2024 / Revised: 22 January 2025 / Accepted: 25 January 2025 / Published: 6 March 2025
(This article belongs to the Special Issue Research on Solar Energy System and Storage for Sustainable Buildings)
Browse Figures
Figure 1
<p>Block diagram of a zero-carbon building’s compound energy system.</p> "> Figure 2
<p>Comparison of annual electricity consumption and power generation of zero-carbon buildings before transformation.</p> "> Figure 3
<p>Monthly electricity consumption and power generation of zero-carbon buildings before transformation.</p> "> Figure 4
<p>Monthly self-absorption rate of the building’s photovoltaic power generation before the transformation.</p> "> Figure 5
<p>Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day before the renovation.</p> "> Figure 6
<p>Usage of photovoltaic power generation on a typical summer’s day before renovation.</p> "> Figure 7
<p>Hourly electricity consumption of zero-carbon buildings on a typical summer’s day before renovation.</p> "> Figure 8
<p>Technical path diagram of day-ahead planning and intraday rolling optimization algorithm.</p> "> Figure 9
<p>Structure of the SSA-CNN-LSTM prediction model.</p> "> Figure 10
<p>Flowchart of the day-ahead planning algorithm model.</p> "> Figure 11
<p>Flowchart of the intraday rolling optimization algorithm.</p> "> Figure 12
<p>Python/TRNSYS multi-energy flow coupling optimization control model of the zero-carbon building’s compound energy system.</p> "> Figure 13
<p>Comparison of annual electricity consumption and power generation of zero-carbon buildings after renovation.</p> "> Figure 14
<p>Monthly electricity consumption and local PV generation of zero-carbon office building after renovation.</p> "> Figure 15
<p>Monthly building self-absorption rate of photovoltaic power generation in zero-carbon office building after renovation.</p> "> Figure 16
<p>Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day after renovation.</p> "> Figure 17
<p>Photovoltaic power generation usage of buildings on a typical summer’s day after renovation.</p> "> Figure 18
<p>Comparison between field test data and load prediction data.</p> "> Figure 19
<p>Supply-side sources of hourly electricity for zero-carbon buildings.</p> "> Figure 20
<p>Utilization schedule of photovoltaic power generation and battery conditions.</p> "> Figure 21
<p>Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation before and after renovation.</p> "> Figure 22
<p>Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation in different seasons before and after renovation.</p> "> Figure 23
<p>Comparison of self-absorption rate of a building’s PV power generation on a typical summer’s day before and after renovation.</p> ">
Versions Notes

Abstract

:
The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system.

1. Introduction

The carbon emission of buildings during operation accounts for about 40% of the total carbon emission of the whole society in China [1]. As an effective way to achieve low-carbon or even zero-carbon emissions in the construction industry, zero-carbon buildings have far-reaching development significance [2]. Zero-carbon buildings aim to avoid the consumption of traditional energy sources such as coal, electricity, and oil, and provide all energy consumption from the renewable energy generated on site [3]. By strengthening the passive energy-saving design of the building envelope and shifting the building’s energy demand to renewable energy sources such as solar energy, wind energy, shallow geothermal energy, and biomass energy, the greenhouse gas emissions are minimized. It does not have any negative impact on climate change [4]. For example, the London Beddington Zero Carbon Community, the China Academy of Building Science Zero Carbon Building, the Shanghai World Expo Zero Carbon Pavilion, etc., are all typical representatives of zero-carbon buildings, which demonstrate the feasibility and advantages of zero-carbon buildings in practical applications. However, the operation and scheduling of the compound energy system in zero-carbon buildings are faced with many challenges. The compound energy system usually contains different types of energy such as electricity, heat, cold, and gas, and there are complex coupling relations among the energy subsystems [5]. In addition, the dynamic energy use characteristics of zero-carbon buildings are different from traditional buildings, and the compound energy system should meet the building’s needs of low carbon, efficient, flexible, reliable, and safe energy demand [6]. The above characteristics put forward high requirements for realizing optimal scheduling strategies for the zero-carbon building’s compound energy systems.
Therefore, it is significant to study the operation scheduling method of the compound energy system in zero-carbon buildings, which can effectively promote the research and application of zero-carbon building technology.
Many scholars around the world have carried out research on the optimization of buildings’ energy systems. Jia et al. [7] present an optimization design method based on “grid-friendly interaction” which optimizes the energy system of zero-energy buildings to ensure better alignment between the building’s electricity purchase/sale and grid demand. Wang et al. [8] studied the integrated building energy system coupled with photovoltaic, wind power, molten salt, solid heat storage, electrode boiler, and battery, and achieved the goal of reducing the total operating cost by 20%. Zeng et al. [9] studied the application method of solar energy seasonal heat storage technology in the Tibetan region of China to solve the time discrepancy problem of solar energy utilization. Li et al. [10] compared different configuration schemes of cogeneration units, wind power generation, ground source heat pump, electricity storage, heat storage, cold storage, and other equipment, and found that energy consumption could be reduced by 3.86% under different schemes. In terms of the operation optimization of buildings coupled with energy systems, Luthander et al. [11] visually analyzed the supply–demand matching performance of distributed PV and buildings’ electricity in different types of buildings through the “supply–demand matching chart”. Sibbitt et al. [12] studied a solar thermal system with borehole cross-season energy storage and improved the system’s operating energy efficiency by 5% through operation optimization. Chu et al. [13] studied the solar-coupled cross-season energy storage system and reduced the operating cost by 8% by optimizing the operating strategy. Jeong et al. [14] made a comparative study on the operation adjustability of the coupled system of solar photovoltaic power generation and ground source heat pump, and the seasonal performance factors increased by 55.3% compared with the single ground source heat pump system by optimizing operation strategies.
Many scholars have carried out related research on coupling control methods of compound energy systems. Zhu Yanmei et al. [15] established an optimal scheduling model that took into account the fluctuations of power generation and output to reduce the volatility of new energy. Wei et al. [16] constructed a functional relationship between the fluctuation of new energy output and the rescheduling of the generator set in view of the scheduling problem of the combined operation system. Li Pai et al. [17] put forward an optimization scheduling model of wind power pumping and storage power station collaboration, which reduces the influence of the randomness of wind power output on the power grid operation. Nystrup et al. [18] proposed a time estimation series method considering auto-correlation and cross-correlation, which significantly improved the accuracy of load prediction. Zhao Fengzhan et al. [19] proposed a multi-time scale energy coordinated optimization scheduling model based on the combination of model prediction and mode decomposition, which effectively improved the operational energy efficiency of the integrated energy system. YILDIRANU et al. [20] proposed a real-time operation method of stochastic predictive control for risk avoidance of the combined power generation system involving the extraction and storage power stations.
In terms of considering demand response and flexible interaction, Daneshvar et al. [21] proposed a two-stage day-ahead and intraday stochastic programming model for the combined operation system of wind power, thermal power, hydropower, extraction, and storage, aiming to maximize the potential of clean energy in the combined operation system. Meng Yan et al. [22] conducted modeling research on the uncertainty of regulating load resources in optimal scheduling. Liu Xiaocong et al. [23] considered the uncertainty of new energy and established a two-stage stochastic programming model to improve the absorption capacity of new energy. Zhao Bo et al. [24] introduced demand response to optimize the power consumption mode of users, improve the load curve, and improve the stability of the system. Wang et al. [25] established an integrated energy system optimization scheduling model with a refined demand response mechanism considering the environment, economic benefits, and energy supply reliability, thus improving the utilization efficiency of integrated energy. Guo et al. [26] applied the demand response strategy to the new renewable hybrid energy power generation system, and studied the effectiveness of the strategy in both typical and extreme situations. Chen Baorui et al. [27] applied an incentive demand response to transmission network expansion planning to reduce costs by reasonably selecting the proportion of demand response. Mingbo et al. [28] took the Longyangxia water/light complementary power station as an object to study the planning of water/light complementary power generation and the method of smoothing PV output fluctuation, respectively. Yin et al. [29] adopted the Copula theory to capture the correlation between the amount of wind power and photovoltaic power generation, and proposed a random scheduling model to carry out day-ahead coordinated scheduling. Karimi et al. [30] established a multi-energy complementary optimal scheduling model, including the pumping and storage units, and demonstrated the service capability of the pumping and storage units to the power grid through the actual data of the power grid. Hu et al. [31] established a two-stage model of day-ahead scheduling and real-time scheduling for the combined wind/water/pumping and storage operating system, and proposed a benefit distribution method based on the improved Shapley value method to improve the overall operating efficiency.
To sum up, although the relevant research on buildings’ compound energy systems has been carried out at home and abroad, the following problems still exist:
  • Most of the research on buildings’ compound energy systems carried out by the building industry focuses on buildings’ cooling and heating systems, failing to cover power generation and storage systems;
  • The power industry has carried out research on energy system scheduling methods based on day-ahead and intraday coupling control, but this method is mainly aimed at power generation equipment, does not take into account the flexible energy use requirements of zero-carbon building terminals, and does not consider the compound characteristics of coupling with cooling and heating systems;
  • Due to the small number of practical projects concerning zero-carbon buildings, the existing research usually focuses on theoretical study and fails to verify the actual effect of the control method in the practical projects of zero- carbon buildings.
In order to solve the above problems, this paper has carried out the research work with highlights from the following aspects:
  • This paper studies the compound energy system of zero-carbon buildings, including photovoltaic, battery, heat pump, heat storage, and other cold and thermal equipment, with a comprehensive system form.
  • The day-ahead and intraday coupling scheduling algorithm established in this paper includes the flexible regulation characteristic requirements of zero-carbon buildings and the coupled operation characteristics of cooling, heating, and electricity, and realizes coupled thermoelectric scheduling.
  • This paper analyzes the actual engineering operational data of a zero-carbon building, which can actually verify the effect of the algorithm model.
Therefore, this study has important practical significance.

2. Materials and Methods

2.1. Composition and Operation of a Typical Zero-Carbon Building’s Compound Energy System

In this paper, an office building in Beijing was selected as a typical zero-carbon office building. This building has 4 floors and its floor area is 4025 m2. This building contains an office, conference room, equipment room, toilet, and stairwell and other functional parts. The building adopts a high-performance envelope structure to reduce energy demand, and the comprehensive heat transfer coefficient of the exterior wall reaches 0.2 W/(m2·k). The comprehensive heat transfer coefficient of the roof reaches 0.25 W/(m2·k). The comprehensive heat transfer coefficient of exterior doors and windows reaches 1.0 W/(m2·k). The heat recovery fresh air unit used in the building has a thermal efficiency of 70%. The intelligent lighting system efficiency, the building airtight grade, and indoor environmental quality meet the requirements of Chinese near zero-energy building standards.
The typical zero-carbon building has compound energy systems, including a photovoltaic power generation system, electricity storage system, ground source heat pump system, air source heat pump system, and water storage system. Through the utilization of an efficient cooling and heating system and a photovoltaic renewable energy system, efforts are made to achieve the annual balance of energy consumption and production in the typical building. The block diagram of the typical zero-carbon building’s compound energy system is shown in Figure 1.
The installed capacity of the roof photovoltaic power generation and the facade photovoltaic power generation in the typical building is 235 kW, and the battery with a capacity of 50 kWh is equipped for demonstration. The photovoltaic power generation is preferentially consumed by the building to meet the building electricity demand, and the excess power generation is sold online. When there is a shortage of on-site renewable energy generation, the building draws electricity from the grid. The office building uses the ground source heat pump system as the main energy system and the air source heat pump as the auxiliary energy system. A low temperature ground source heat pump unit with a rated cooling capacity of 50 kW and a rated heat capacity of 52 kW is used to meet the fresh air load in the building. A high-temperature ground source heat pump unit with a rated cooling capacity of 105 kW and a rated heat capacity of 103 kW and a water thermal storage system are used to meet the cooling and heating load in the building.
Before December 2023, the scheduling strategy of the compound energy system in the typical zero-carbon building was based on the TOU electricity price policy and manual experience control. According to the building’s average energy consumption data of the TOU peak hours in the previous week, the property staff operates energy systems for thermal storage and electricity storage at night during the TOU valley hours, and gives priority to energy release during the peak hours. Beijing’s TOU pricing policy applies to industrial and commercial users. The table of TOU peak and valley electricity prices in Beijing is shown in Table 1.
According to the annual actual operational data of zero-carbon buildings from December 2022 to November 2023, the actual monitoring data of annual photovoltaic power generation and the electricity consumption of the building are shown in Figure 2 and Table 2. The total annual power generation of photovoltaic system per unit building area is 41.93 kWh/(m2·a). The annual electricity consumption per unit area of the building is 41.61 kWh/(m2·a). According to the annual data analysis, the total amount of on-site renewable energy power generation can meet the total electricity demand of the building. In terms of theoretical calculation, the carbon emissions generated by the building’s energy consumption and the carbon reduction amount of renewable energy power generation are balanced annually.
When analyzing the actual utilization amount of photovoltaic power generation, this paper adopts the self-absorption rate of the building’s renewable energy generation as the analysis index. The part of photovoltaic power generation that is not sold online is considered to be the local self-consumption amount of photovoltaic power generation. The calculation formula of the self-absorption rate of the building’s photovoltaic power generation is shown in Formula (1). The actual local photovoltaic power generation consumed by typical zero-carbon buildings throughout the year accounted for 81.18% of the total annual photovoltaic power generation (excess photovoltaic power generation was sold online), and the carbon emission balance of the buildings was not achieved in the actual operation process.
P C O N S U M E = P g e n e r a t e − P s e l l P g e n e r a t e   =   81.18 %
where P C O N S U M E indicates the self-absorption rate of the building’s photovoltaic power generation, P s e l l indicates the photovoltaic power generation sold online, and P g e n e r a t e indicates the total photovoltaic power generation.
When we analyze the monthly energy system operational data of this typical zero-carbon building, the monthly electricity consumption and local photovoltaic power generation of the building are shown in Figure 3. The monthly self-absorption rate of the building’s photovoltaic power generation is shown in Figure 4. In the cooling season (June–August), the HVAC electricity demand is high, among which the self-absorption rate of the building’s photovoltaic power generation is 91.61%. The self-absorption rate of the building’s photovoltaic power generation in the heating season is 81.30%. The monthly electricity demand of transitional seasonal buildings is low, and the self-absorption rate of the building’s photovoltaic power generation is 62.82%.
When we analyze the operation of the energy system of the zero-carbon building on a typical cooling day in June 2023 when the office hours of the building are 8:30–17:30, the matching between power consumption and renewable power generation is shown in Figure 5. The usage of photovoltaic power generation on a typical summer’s day before renovation is shown in Figure 6. The total power consumption of the building is 943.19 kWh, and the total photovoltaic power generation is 840.42 kWh. Theoretically, the building’s power consumption is greater than the photovoltaic power generation for the whole day. However, the photovoltaic power generation sold online is 94.43 kWh, and the self-absorption rate of the building’s photovoltaic power generation throughout the day is 90.22%.
Through the analysis of the hourly electricity consumption of a typical day in the cooling season, as shown in Figure 7, we found that, at night, the project uses the valley price electricity for power storage and releases electricity during the peak price period of the next day. The peak hours of electricity consumption start from 11:00, basically matching the peak hours of photovoltaic power generation. However, due to the unreasonable operational strategy of the building, the thermal storage and electricity storage systems released energy between 11:00 and 13:00, resulting in the building’s electricity consumption being reduced during this time, as photovoltaic power generation cannot be consumed or stored in time.
From the above analysis, it can be seen that, although the annual overall data analysis shows that the zero-carbon building is equipped with a sufficient photovoltaic renewable energy capacity, due to the unreasonable operational scheduling strategy, the actual self-absorption ratio of the building’s photovoltaic generation fails to meet the design expectation.

2.2. Energy System Control Scheduling Algorithm Considering Day-Ahead Flexible Programming and Intraday Rolling Optimization

2.2.1. Overall Technical Path

In order to realize the intelligent control goal of the zero-carbon building’s compound energy system, this paper proposes a day-ahead planning and intraday rolling optimization algorithm considering the flexibility and adjustable ability of the building. The overall technical path of the algorithm is shown in Figure 8. Based on the prediction of the building’s cooling/heating/power load and renewable power generation, the day-ahead planning takes 24 h as a cycle and 1 h as a scale step. Considering the time-of-use electricity price, equipment operation, and energy balance constraints, the unit start–stop plan, the charging and discharging plan of electricity storage and water thermal storage equipment, the flexible load transfer, and the reduction scheduling plan of the building energy system are developed. Based on day-ahead planning, intraday rolling optimization takes 24 h as a cycle and 1 h as a scale step. Considering the difference between the actual energy demand of the building and the predicted results, the operational scheduling strategy of the building energy system is modified through rolling optimization to improve the energy efficiency of the system and reduce the carbon emission [32].

2.2.2. Load Forecasting Algorithm

The prediction of the building’s load and photovoltaic power generation is the basis of the building’s energy system operational scheduling control. In this paper, the coupling of the sparrow search algorithm (SSA) model, the convolutional neural network (CNN), and LSTM (long short-term memory network) is used to predict the load, which improves the accuracy and stability of the prediction on both sides of the renewable power generation and source load. First, the SSA algorithm is used to carry out a dynamic update global search, optimize the hyperparameters of CNN and LSTIM, and rapidly narrow the range of global optimal solutions. Then, the CNN model is used to locally extract important features in time series prediction to provide rich input information for LSTM. Finally, the long-term dependency relationship is further explored through LSTM. Through the combination of SSA optimization, CNN local feature extraction, and LSTM long-term dependence capture, the model can improve prediction accuracy and efficiency in compound prediction tasks, and complete the building’s 24 h hourly electricity, heat, and cooling load prediction and hourly photovoltaic power generation [33]. The structure of the SSA-CNN-LSTM prediction model is shown in Figure 9.
The prediction method adopted in this paper needs to collect historical data such as outdoor temperature, humidity, solar irradiance, wind speed, building’s cooling load, building’s heating load, building’s electricity load, and renewable energy generation as input conditions, and forecast the load at different time scales such as the next 24 h and 7 days according to the information of weather forecast meteorological parameters and the building’s operation time schedule. The more information a project can collect, the more accurate the prediction can be.

2.2.3. Day-Ahead Planning Algorithm Considering Flexible Adjustment Ability

According to the forecast results of the building’s heating/cooling/power load and renewable energy power generation, the day-ahead planning of the operational strategy of the building compound energy system is carried out, which considers the flexible adjustment ability of the zero-carbon building energy system, TOU peak and valley electricity price, and the demand response plan of construction activities. The day-ahead planning model flow diagram is shown in Figure 10. The day-ahead planning algorithm proposed in this paper is a day-ahead robust optimal scheduling method considering the uncertainty of renewable power generation and the building’s demand response. The calculation model of this method takes the renewable energy generation prediction results and the building’s load prediction results as input conditions, constructs the energy system operation model according to the energy consumption schedule of the building, the flexible load demand response characteristics, and the electricity price schedule information of the peak and valley of the building, and optimizes with the goal of the lowest energy consumption of the system. The specific scheduling strategy generated by the day-ahead planning algorithm includes the start and stop plan of energy system units, the time plan of thermal storage equipment and battery storage and release, and the operation time plan of flexible equipment.
According to the way of participating in the energy system demand response process, the flexible load of zero-carbon buildings can be divided into translational load, transferable load, and reducible load. The translational load is a load with fixed power and continuous working time in a certain period of time, which needs to continue to consume energy without interruption after starting, and can be shifted to multiple periods. The transferable load is a type of load with adjustable power and duration during the cycle under the condition that the total load remains unchanged. Reducible loads are loads that can reduce power, shorten time, or interrupt operation to a certain extent. This paper analyzes the composition of flexible load equipment in typical zero-carbon buildings, and the classification is shown in Table 3. [34]
Day-ahead planning considers the schedule of construction activities and the schedule of peak and valley electricity prices, takes the lowest electricity cost of the system operation as the optimization goal, takes into account the characteristics of the zero-carbon building’s load and flexible load, and obtains the hourly output plan of the battery, water storage, heat pump system, photovoltaic power generation, building’s flexible energy use equipment in the next 24 h through optimization calculation. In the day-ahead stage, it is necessary to determine the start–stop plan of the main unit, the transferable flexible load response amount of the day-ahead energy storage equipment, and the reducible and transferable load response amount of the building operation, and substitute it into the day-ahead planning as the determined quantity. In the day-ahead stage, unit operation energy consumption, unit start–stop loss, TOU electricity price cost, day-ahead flexible response energy consumption, and light abandonment penalty cost should be taken into account, and the day-ahead objective function should be constructed with the lowest system operation electricity cost, as shown in Formula (2) [35].
F A L L D A = ∑ t = 1 24 { ∑ i = 1 N U i , t a i P G i , t 2 + b i P G i , t + c i + ∑ i = 1 N U i , t 1 − U i , t − 1 + U i , t − 1 1 − U i , t − 1 F i + ∑ i = 1 N C t 24 + F c o s t s f t , D A P t s f t , D A + F c o s t t r a n , D A P t t r a n , D A + F c o s t c u t , D A P t c u t , D A + K R P R , t p r e , 1 − P R , t }
F A L L D A is the total day-ahead system cost, U i , t is the state variable of unit i at time t, a i   b i   c i is the operating cost parameter of unit i, P G i , t is the power of unit i at time t, F i is the start–stop cost of unit, F c o s t s f t , D A   F c o s t t r a n , D A F c o s t c u t , D A and P t s f t , D A   P t t r a n , D A P t c u t , D A are the compensation coefficient and response quantity before the load can be shifted, transferred, and reduced, respectively, and K R is the penalty coefficient of light abandonment. Respectively, P R , t p r e , 1 and P R , t are the photovoltaic forecast power and absorption power.

2.2.4. Intraday Rolling Optimization Algorithm

Figure 11 shows the flowchart of the intraday rolling optimization algorithm. The intraday rolling optimization model analyzes the difference between the day-ahead forecast load data and the actual operational data of the same day, forecasts the intraday load according to the actual operation schedule and other information, and completes the generation of the control strategy of the detailed operation parameters of the equipment. The intraday rolling optimization is based on the actual generation situation of renewable energy monitored in real time (such as the current actual photovoltaic output, the real-time power of wind turbines, etc.) and the actual load change of the building (which may be caused by the temporary addition of large electrical equipment, the change in the number of personnel, etc.). The energy scheduling plan is revised every 1 h for the rest of the period. Based on the day-ahead planning start–stop plan of the compound energy system, the charging and discharging plan of the energy storage equipment, the output plan of the energy equipment, and the scheduling plan of the demand response of the portable electric load, based on the real-time updated renewable power generation output and the ultra-short-term forecast data of the power of the electric hot and cold loads, predictive control is used to build the output prediction model of all controllable equipment in a limited period of time in the future, further establish the intraday rolling optimization scheduling model of the regional integrated energy system with the minimum output deviation and regulation amount as the goal, and carry out the intraday optimization control of the battery, energy storage water tank, cold and heat source system equipment, and building flexible energy use equipment with the set parameter levels on an hourly basis.
In the intraday rolling optimization scheduling, the previously completed power storage and water thermal storage related plans are no longer considered. According to the deviation of load forecast and actual energy supply and demand, the intraday load adjustment response cost is considered extra. In addition, the penalty cost of abandoning light also changes due to the change in the cheap situation. Therefore, the objective function is constructed with the lowest operating power cost of the system, as shown below, to complete the control optimization on the operating parameter level. The intraday objective function based on the lowest electricity cost for system operation is shown in Formula (3) [36].
F A L L I D = ∑ t = 1 T 1 { ∑ i = 1 N U i , t a i P G i , t 2 + b i P G i , t + c i + ∑ i = 1 N C t + F c o s t s f t , I D P t s f t , I D + F c o s t t r a n , I D P t t r a n , I D + F c o s t c u t , I D P t c u t , I D                                       + K R P R , t p r e , 2 − P R , t }
F A L L I D is the total intraday system cost, T 1 is the intraday rolling optimization scheduling cycle, F c o s t s f t , I D   F c o s t s f t , I D F c o s t c u t , I D and P t s f t , I D P t s f t , I D P t c u t , I D are the intraday flexible compensation coefficient and the intraday flexible response quantity that can be translated, transferred, and reduced, respectively, P R , t p r e , 2 is the intraday rolling forecast power in PV.

2.3. Python/TRNSYS-Based Multi-Energy Flow Coupling Optimization Control Model

In this paper, Python programming and a TRNSYS ver.18 software dynamic simulation are combined to build a multi-energy flow coupling optimization control model for the zero-carbon building’s compound energy system, as shown in Figure 12. The establishment of a multi-energy flow model of the zero-carbon building’s compound energy system is the basis for realizing the control optimization calculation. In this paper, TRNSYS ver.18 software is used for modular modeling advantages to complete the establishment of the photovoltaic power generation model, battery storage model, heat pump system model, water storage tank model, and the zero-carbon building’s energy equipment model. The coupling operation relationship between the different systems is considered in the model. An 8760 h dynamic simulation of the zero-carbon building’s compound energy system is realized in this model. In this paper, Python is used to complete the load prediction algorithm programming of the building’s power consumption, heating and cooling loads, and photovoltaic power generation, along with the day-ahead flexible planning and intraday rolling optimization algorithm programming, in order to realize the global optimization control of the compound energy system.
The multi-energy flow coupling optimization control model is packaged in the building’s energy control interactive software. The construction of the building’s energy control interactive software for the typical zero-carbon building was carried out in December 2023, and the control algorithm proposed in this paper was actually applied in the energy system control process of the typical zero-carbon building.

3. Results

The coupling optimization control model proposed in this paper has been formally applied to the operational scheduling control of the compound energy system of the typical zero-carbon building. The actual operational scheduling data for 1 year from December 2023 to November 2024 are analyzed below.

3.1. Analysis of the Operation of the Energy System After Transformation Based on Monitoring Data

According to the actual operational scheduling data from December 2023 to November 2024, the actual monitoring data of the annual photovoltaic power generation and the building’s electricity consumption of the typical zero-carbon office building are shown in Figure 13. The total power generation of the photovoltaic system per unit building area is 42.33 kWh/(m2·a). The annual electricity consumption per unit area of the building is 41.57 kWh/(m2·a). According to the annual data analysis, the total amount of renewable energy power generation can meet the total electricity demand of the building, the carbon emissions generated by the building’s energy consumption, and the carbon reduction amount of renewable energy power generation are balanced annually, As shown in Table 4, the self-absorption rate of photovoltaic power generation is 88.31%.
When we analyze the monthly energy system operational data of the typical zero-carbon building after transformation, the monthly electricity consumption and photovoltaic power generation of the zero-carbon office building are shown in Figure 14, and the matching between electricity consumption and power generation is shown in Figure 15. In the cooling season (June to August), the demand for HVAC electricity is high, and the self-absorption rate of photovoltaic power generation buildings is 97.43%. The self-absorption rate of photovoltaic power generation in the heating season is 89.54%. The monthly electricity demand of transitional seasonal buildings is low, and the self-absorption rate of photovoltaic power generation buildings is 69.32%.
In this paper, 27 June 2024 is selected as a typical cooling day to analyze the operational effect of the zero-carbon building’s energy system. The power consumption and photovoltaic power generation are shown in Figure 16, and the use of photovoltaic power generation in the building on the typical day is shown in Figure 17. The total electricity consumption of the building is 931.03 kWh, the total daily photovoltaic power generation is 805.94 kWh, the photovoltaic power generation sold online is 17.53 kWh, and the self-absorption rate of photovoltaic power generation in the building is 97.82%.

3.2. Analysis of Operation Control Effect of Energy System After Renovation Based on Field Measurement

In this paper, 27 June 2024 is selected as a typical summer’s day for the zero-carbon office building, and the 24 h operational data of the compound energy system are tested on site to analyze the effect of the operational control strategy.

3.2.1. Prediction of the Building’s 24 h Electricity Load and Photovoltaic Power Generation

This project carried out on-site energy system testing, and obtained 24 h system hourly power consumption and photovoltaic power generation data. The data were compared with the 24 h load prediction data exported from the building’s energy control interactive software, and the results are shown in Figure 18. The average deviation of the building’s hourly electricity consumption between the predicted results and the test results is 4.53%, the average deviation of hourly photovoltaic power generation between the predicted results and the test results is 4.77%, and the trend is consistent from time to time, so the accuracy of the prediction results is high.

3.2.2. Analysis of the Energy System’s 24 h Photovoltaic Power Generation Utilization Strategy

Figure 19 shows the supply-side source of hourly electricity consumption of zero-carbon buildings, and Figure 20 shows the schedule of storage and consumption of photovoltaic power generation by making full use of the flexible adjustment ability of batteries. According to the data of the 24 h load prediction, the batteries are charged by municipal electricity from 23:00 to 5:00. In order to utilize as much photovoltaic power generation as possible during the peak hours of photovoltaic power generation at noon, the battery will be fully discharged from 8:00 a.m. to 10:00 a.m., the photovoltaic power generation that cannot be consumed by the building will be stored from 11:00 p.m. to 13:00 p.m., and the discharge will be carried out at 17:00–19:00 to maximize the utilization of photovoltaic power generation. The operational scheduling strategy completes the flexible storage and absorption of 49 kWh photovoltaic power generation, and effectively improves the self-absorption ratio of 6.08% of the building’s photovoltaic power generation.

3.3. Comparison of Energy System Operation Before and After Renovation

3.3.1. The Annual Self-Absorption Rate of the Building’s Photovoltaic Power Generation Increased

As shown in Figure 21, from December 2022 to November 2023, the annual PV self-absorption rate of typical zero-carbon buildings before renovation is 81.18%, and from December 2023 to November 2024, the annual PV self-absorption rate of typical zero-carbon buildings after renovation is 88.31%—an increase of 7.13%.

3.3.2. The Seasonal Self-Absorption Rate of Photovoltaic Power Generation Increased

As shown in Figure 22, the self-absorption rate of photovoltaic power generation in the cooling season of a typical zero-carbon building after the transformation increased by 8.47% compared with that before the transformation, the self-absorption rate of photovoltaic power generation in the cooling season increased by 8.24% compared with that before the transformation, and the self-absorption rate of photovoltaic power generation in the transition season increased by 6.50% compared with that before renovation.

3.3.3. The Self-Absorption Rate of Photovoltaic Power Generation on a Typical Summer’s Day Increased

As shown in Figure 23, before the transformation, the self-absorption rate of a building’s PV power generation on a typical day in summer is 90.22%, and after the transformation, the self-absorption rate of a building’s PV power generation on a typical day in summer is 97.82%—an increase of 7.60%.

4. Conclusions

Aiming at the problem that the operational scheduling control method of zero-carbon building’s compound energy system lacks actual data support, mature theoretical guidance, and algorithm model tools, this paper selects a typical zero-carbon building practical project as the research object, and proposes an energy system control scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. A multi-energy flow coupling optimization control model is developed, which can be used in practical engineering. By applying the control algorithm model to the typical building, this paper makes a comparative analysis of the operational scheduling data of the zero-carbon building’s energy system before and after renovation. The overall conclusion is as follows:
  • This paper proposes an energy system control algorithm based on day-ahead flexible planning and intraday rolling optimization that is suitable for zero-carbon buildings, and develops a multi-energy flow coupling optimization control model for heat pump, photovoltaic, electricity storage, and thermal storage combined systems, and this algorithm model can be applied to practical projects. It provides a reproducible algorithm model tool for the operational scheduling control of a zero-carbon building’s compound energy system.
  • This paper analyzes the two-year operational scheduling data of the zero-carbon building project, and the results show that the annual self-absorption rate of the building’s photovoltaic power generation is increased by 7.13% after utilizing the flexible adjustment ability of equipment such as electricity storage. The algorithm model established in this study has a good effect of improving the utilization of renewable energy.
  • This paper provides actual data for the research and engineering construction of zero-carbon buildings’ compound energy systems, which could help promote the compound system application of photovoltaic, heat pump, and energy storage systems. Through in-depth research on the integration and cooperative scheduling of zero-carbon buildings’ energy systems, the renewable energy utilization effect of buildings can be effectively improved and the effect of buildings in reducing carbon emissions can be guaranteed.
However, although this paper has proposed the energy system control algorithm and control model, there are still shortcomings, which need to be addressed in the follow-up research. First, the multi-energy flow coupling optimization control model developed in this paper only includes heat pump, photovoltaic, battery, water storage, and buildings’ end-use energy systems, and cannot cover all types of building energy equipment. The follow-up studies should expand the equipment types of the multi-energy flow system model and improve the scope of application of the model. Second, this paper only conducted effect verification for one specific building in Beijing, and the amount of operational data are not rich enough. The application effect analysis should be carried out in more practical projects in the future, and the algorithm model should be continuously trained and improved through actual data.

Author Contributions

Conceptualization, B.Q. and J.D.; methodology, B.Q. and W.X.; data analysis, B.Q.; investigation, B.Q. and J.L.; writing—original draft, B.Q.; writing—review and editing, B.Q. and F.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Building Energy Efficiency Research Center, Tsinghua University. China Building Energy Efficiency Annual Development Research Report 2023; China Building and Construction Press: Beijing, China, 2023; pp. 12–32. [Google Scholar]
  2. Ministry of Housing and Urban-Rural Development. Urban and Rural Construction Field of Peak Carbon Plan. 30 June 2022. Available online: https://www.gov.cn/zhengce/zhengceku/2022-07-13/content_5700752.htm (accessed on 30 March 2024).
  3. Zhang, S.; Liu, C.; Wang, K.; Chen, X.; Xu, W. Research on the definition of zero-carbon buildings and carbon emission calculation boundary. Build. Sci. 2022, 38, 283–290. (In Chinese) [Google Scholar] [CrossRef]
  4. World Green Building Council. The Net Zero Carbon Buildings Commitment. World Green Building Council. 20 April 2020. Available online: https://www.worldgbc.org/ (accessed on 30 March 2024).
  5. Jiang, Y. Light storage direct and soft: A new building distribution system to help realize zero-carbon electricity. HVAC 2019, 51, 1–12. [Google Scholar]
  6. Liu, X.; Zhang, T.; Liu, X. How to describe the basic characteristics of the building in the new power system?—The status quo and prospect. J. HVAC 2023, 53, 1–10+124. [Google Scholar] [CrossRef]
  7. Jia, S.; Sheng, K.; Huang, D.; Hu, K.; Xu, Y.; Yan, C. Design optimization of energy systems for zero energy buildings based on grid-friendly interaction with smart grid. Energy 2023, 284, 129298. [Google Scholar] [CrossRef]
  8. Wang, Q.; Wang, B.; Liu, X. Optimal allocation of integrated energy system in Industrial Park under zero Carbon trading. Zhejiang Univ. J. Eng. Technol. 2023, 57, 2294–2304. [Google Scholar]
  9. Zeng, Q.; Li, L.; Chen, X.; Tian, Z.; Mao, H.; Luo, Y.; Zhou, C.; Fan, J. Techno-economic performance and optimization of a large solar district heating system with pit storage under Tibetan Plateau climate conditions. Energy 2025, 315, 134449. [Google Scholar] [CrossRef]
  10. Li, H.; Zhang, C.; Sun, B. Optimal design and operation method of integrated energy system based on stochastic scenario iteration and energy storage attribute evaluation. Sustain. Cities Soc. 2023, 95, 104585. [Google Scholar] [CrossRef]
  11. Luthander, R.; Nilsson, A.M.; Widen, J.; Åberg, M. Graphical analysis of photovoltaic generation and load matching in buildings: A novel way of studying self-consumption and self-sufficiency. Appl. Energy 2019, 250, 748–759. [Google Scholar] [CrossRef]
  12. Sibbitt, B.; McClenahan, D.; Djebbar, R.; Thornton, J.; Wong, B.; Carriere, J.; Kokko, J. The performance of a high solar fraction seasonal storage district heating system-five years of operation. Energy Procedia 2012, 30, 856–865. [Google Scholar] [CrossRef]
  13. Chu, S.; Sethuvenkatraman, S.; Goldsworthy, M.; Yuan, G. Techno-economic assessment of solar assisted precinct level heating systems with seasonal heat storage for Australian cities. Renew. Energy 2022, 201, 841–853. [Google Scholar] [CrossRef]
  14. Jeong, Y.; Yu, M.; Nam, Y. Feasibility study of a heating, cooling and domestic hot water system combining a photovoltaic-thermal system and a ground source heat pump. Energies 2017, 10, 1243. [Google Scholar] [CrossRef]
  15. Zhu, Y.; Chen, S.; Ma, G.; Han, X.Y. Wang, L. Water-light complementary short-term scheduling with Power Generation and output fluctuations. Trans. China Electrotech. Soc. 2020, 35, 2769–2779. [Google Scholar]
  16. Wei, W.; Chen, Y.; Wang, C.; Wu, Q.; Shahidehpour, M. Nodal flexibility requirements for tackling renewable power fluctuations. IEEE Trans. Power Syst. 2021, 36, 3227–3237. [Google Scholar] [CrossRef]
  17. Li, P.; Wang, W.; Liu, C.; Huang, Y.; Wang, Y.; Zhang, L. Economic Evaluation of New energy and Extraction and storage power Station configuration Scheme in Zhangbei Flexible DC Power Grid Project. Proc. CSEE 2018, 38, 7206–7214. [Google Scholar]
  18. Nystrup, P.; Lindstrom, E.; Pinson, P.; Madsen, H. Temporal hierarchies with autocorrelation for load forecasting. Eur. J. Oper. Res. 2019, 280, 876–888. (In Chinese) [Google Scholar] [CrossRef]
  19. Zhao, F.; Zhang, Q.; Zhang, Y.; Du, S.; Hao, S.; Su, J.; Zhao, T. Multi-time scale Energy Coordinated optimization Scheduling for grid-connected microgrid based on VMD-MPC Method. Trans. Chin. Soc. Agric. Eng. 2021, 37, 190–198. [Google Scholar]
  20. Yildiran, U.; Kayahan, I. Risk-averse stochastic model predictive control-based realtime operation method for a wind energy generation system supported by a pumped hydro storage unit. Appl. Energy 2018, 226, 631–643. [Google Scholar] [CrossRef]
  21. Daneshvar, M.; Mohammadi-Ivatloo, B.; Zare, K.; Asadi, S. Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment. Energy 2020, 193, 969–981. [Google Scholar] [CrossRef]
  22. Meng, Y.; Xiao, J.; Hong, J.; Zhang, L.; Fan, S.; He, G. Node load alignment taking demand response uncertainty into account: Concept and model. Autom. Electr. Power Syst. 2023, 47, 28–39. [Google Scholar]
  23. Liu, X.; Wang, B.; Li, Y.; Wang, K. Random Unit Combination model for large-scale wind power consumption with demand-Side Resources. Trans. CSEE 2015, 35, 3714–3723. [Google Scholar]
  24. Zhao, B.; Wang, X.; Zhang, X.; Zhou, J. Two-layer optimization of microgrid considering demand side response and uncertainty. J. Electr. Technol. 2018, 33, 3284–3295. (In Chinese) [Google Scholar]
  25. Wang, Y.; Huang, Y.; Wang, Y.; Zeng, M.; Yu, H.; Li, F.; Zhang, F. Optimal scheduling of the RIES considering timebased demand response programs with energy price. Energy 2018, 164, 773–793. [Google Scholar] [CrossRef]
  26. Guo, M.H.; Wang, W.; Chen, R.H. Renewable hybrid energy system scheduling strategy considering demand response. Sustain. Energy Technol. Assess. 2022, 52, 102247. [Google Scholar] [CrossRef]
  27. Chen, B.-R.; Liu, T.-Q.; He, C.; Hu, X.T.; Su, X.N. Long term expansion planning of source load coordinated Branch rod considering Demand Response. Proc. CSEE 2021, 41, 6886–6900. [Google Scholar]
  28. Ming, B.; Liu, P.; Cheng, L.; Zhou, Y.; Wang, X. Optimal daily generation scheduling of large hydrophotovoltaic hybrid power plants. Energy Convers. Manag. 2018, 171, 528–540. [Google Scholar] [CrossRef]
  29. Yin, Y.; Liu, T.; He, C. Day-ahead stochastic coordinated scheduling for thermal-hydrowind-photovoltaic systems. Energy 2019, 187, 115944. [Google Scholar] [CrossRef]
  30. Karimi, A.; Heydari, S.L.; Kouchakmohseni, F.; Naghiloo, M. Scheduling and value of pumped storage hydropower plant in Iran power grid based on fuel-saving in thermal units. Energy Storage 2019, 24, 100753. [Google Scholar] [CrossRef]
  31. Hu, S.; Xiang, Y.; Liu, J.Y.; Liu, C. A two-stage dispatching method for wind-hydropowerpumped storage integrated power systems. Front. Energy Res. 2021, 9, 646975. [Google Scholar] [CrossRef]
  32. Li, R.; Si, Y.; Yang, C.; Li, X. Low-Carbon Optimal Scheduling for New Power Systems Considering Flexible Load Before and After Load. Southern Power Grid Technology. pp. 1–15. Available online: http://kns.cnki.net/kcms/detail/44.1643.tk.20240715.1159.011.html (accessed on 23 December 2024).
  33. Kong, X.; Ma, Y.; Ai, Q.; Zhang, X.; Li, C.; Xiao, B. Review of electricity characteristic modeling and load forecasting for multiple users of new power systems. Power Syst. Autom. 2023, 47, 2–17. [Google Scholar]
  34. Wang, G.; Li, Z.; Liu, C. Flexible load aggregation method for evaluating the flexibility of multiple time granularity robust. Electr. Power Autom. Equip. 2023, 43, 167–174+203. [Google Scholar] [CrossRef]
  35. Cheng, Q.; Luo, P.; Liu, P.; Li, X.; Ming, B.; Huang, K.; Xu, W.; Gong, Y. Stochastic short-term scheduling of a wind-solar-hydro complementary system considering both the day-ahead market bidding and bilateral contracts decomposition. Int. J. Electr. Power Energy Syst. 2022, 138, 107904. [Google Scholar] [CrossRef]
  36. Zacharias, C.; Liu, N.; Begen, M.A. Dynamic interday and intraday scheduling. Oper. Res. 2024, 72, 317–335. [Google Scholar] [CrossRef]
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Figure 1. Block diagram of a zero-carbon building’s compound energy system.
Figure 1. Block diagram of a zero-carbon building’s compound energy system.
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Figure 2. Comparison of annual electricity consumption and power generation of zero-carbon buildings before transformation.
Figure 2. Comparison of annual electricity consumption and power generation of zero-carbon buildings before transformation.
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Figure 3. Monthly electricity consumption and power generation of zero-carbon buildings before transformation.
Figure 3. Monthly electricity consumption and power generation of zero-carbon buildings before transformation.
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Figure 4. Monthly self-absorption rate of the building’s photovoltaic power generation before the transformation.
Figure 4. Monthly self-absorption rate of the building’s photovoltaic power generation before the transformation.
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Figure 5. Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day before the renovation.
Figure 5. Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day before the renovation.
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Figure 6. Usage of photovoltaic power generation on a typical summer’s day before renovation.
Figure 6. Usage of photovoltaic power generation on a typical summer’s day before renovation.
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Figure 7. Hourly electricity consumption of zero-carbon buildings on a typical summer’s day before renovation.
Figure 7. Hourly electricity consumption of zero-carbon buildings on a typical summer’s day before renovation.
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Figure 8. Technical path diagram of day-ahead planning and intraday rolling optimization algorithm.
Figure 8. Technical path diagram of day-ahead planning and intraday rolling optimization algorithm.
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Figure 9. Structure of the SSA-CNN-LSTM prediction model.
Figure 9. Structure of the SSA-CNN-LSTM prediction model.
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Figure 10. Flowchart of the day-ahead planning algorithm model.
Figure 10. Flowchart of the day-ahead planning algorithm model.
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Figure 11. Flowchart of the intraday rolling optimization algorithm.
Figure 11. Flowchart of the intraday rolling optimization algorithm.
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Figure 12. Python/TRNSYS multi-energy flow coupling optimization control model of the zero-carbon building’s compound energy system.
Figure 12. Python/TRNSYS multi-energy flow coupling optimization control model of the zero-carbon building’s compound energy system.
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Figure 13. Comparison of annual electricity consumption and power generation of zero-carbon buildings after renovation.
Figure 13. Comparison of annual electricity consumption and power generation of zero-carbon buildings after renovation.
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Figure 14. Monthly electricity consumption and local PV generation of zero-carbon office building after renovation.
Figure 14. Monthly electricity consumption and local PV generation of zero-carbon office building after renovation.
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Figure 15. Monthly building self-absorption rate of photovoltaic power generation in zero-carbon office building after renovation.
Figure 15. Monthly building self-absorption rate of photovoltaic power generation in zero-carbon office building after renovation.
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Figure 16. Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day after renovation.
Figure 16. Hourly electricity demand and photovoltaic power generation of zero-carbon buildings on a typical summer’s day after renovation.
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Figure 17. Photovoltaic power generation usage of buildings on a typical summer’s day after renovation.
Figure 17. Photovoltaic power generation usage of buildings on a typical summer’s day after renovation.
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Figure 18. Comparison between field test data and load prediction data.
Figure 18. Comparison between field test data and load prediction data.
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Figure 19. Supply-side sources of hourly electricity for zero-carbon buildings.
Figure 19. Supply-side sources of hourly electricity for zero-carbon buildings.
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Figure 20. Utilization schedule of photovoltaic power generation and battery conditions.
Figure 20. Utilization schedule of photovoltaic power generation and battery conditions.
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Figure 21. Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation before and after renovation.
Figure 21. Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation before and after renovation.
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Figure 22. Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation in different seasons before and after renovation.
Figure 22. Comparison of self-absorption rate of zero-carbon building’s photovoltaic power generation in different seasons before and after renovation.
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Figure 23. Comparison of self-absorption rate of a building’s PV power generation on a typical summer’s day before and after renovation.
Figure 23. Comparison of self-absorption rate of a building’s PV power generation on a typical summer’s day before and after renovation.
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Table 1. Beijing TOU peak and valley electricity price table.
Table 1. Beijing TOU peak and valley electricity price table.
Time-of-Use PeriodTimePrice (CNY/kWh)
Summer peak hours11:00–13:00, 16:00–17:001.32
Peak hours10:00–11:00, 17:00–22:001.17
Flat hours07:00–10:00, 13:00–16:00,
22:00–23:00		0.85
Valley hours23:00–07:000.56
Table 2. Annual itemized equipment electricity consumption of zero- carbon buildings before renovation.
Table 2. Annual itemized equipment electricity consumption of zero- carbon buildings before renovation.
CategoryData kWh/(m2·a)
HVAC power consumption21.31
Lighting power consumption8.12
Other power consumption12.18
PV power generation41.93
PV power online sales 7.89
Table 3. Classification of the building’s flexible load.
Table 3. Classification of the building’s flexible load.
Flexible Load TypeTypical Equipment
Reducible loadLighting, some socket outlets to use electricity
Translational loadOverall movement, non-deformation + non-variable + non-interruptible load; some socket equipment, printers, etc.
Transferable loadDeforming + non-variable; battery, charging pile, energy storage tank; disable + variable; HVAC, heat pump system, air conditioning system
Table 4. Annual itemized electricity statistics of the zero-carbon building after renovation.
Table 4. Annual itemized electricity statistics of the zero-carbon building after renovation.
Category Data kWh/(m2·a)
HVAC power consumption20.74
Lighting power consumption8.17
Other power consumption12.66
PV power generation42.33
PV power online sales 4.95
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MDPI and ACS Style

Qiao, B.; Dong, J.; Xu, W.; Li, J.; Lu, F. Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm. Buildings 2025, 15, 836. https://doi.org/10.3390/buildings15050836

AMA Style

Qiao B, Dong J, Xu W, Li J, Lu F. Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm. Buildings. 2025; 15(5):836. https://doi.org/10.3390/buildings15050836

Chicago/Turabian Style

Qiao, Biao, Jiankai Dong, Wei Xu, Ji Li, and Fei Lu. 2025. "Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm" Buildings 15, no. 5: 836. https://doi.org/10.3390/buildings15050836

APA Style

Qiao, B., Dong, J., Xu, W., Li, J., & Lu, F. (2025). Research and Demonstration of Operation Optimization Method of Zero-Carbon Building’s Compound Energy System Based on Day-Ahead Planning and Intraday Rolling Optimization Algorithm. Buildings, 15(5), 836. https://doi.org/10.3390/buildings15050836

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