Optimization Design and Operation Analysis of Integrated Energy System for Rural Active Net-Zero Energy Buildings

Abstract

To address energy shortages and achieve carbon peaking/neutrality, this study develops a distributed renewable-based integrated energy system (IES) for rural active zero-energy buildings (ZEBs). Energy consumption patterns of typical rural houses are analyzed, guiding the design of a resource-tailored IES that balances economy and sustainability. Key equipment capacities are optimized to achieve net-zero/zero energy consumption targets. For typical daily cooling/heating/power loads, equipment output is scheduled using a dual-objective optimization model minimizing operating costs and CO₂ emissions. Results demonstrate that: (1) Net-zero-energy IES outperforms separated production (SP) and full electrification systems (FES) in economic-environmental benefits; (2) Zero-energy IES significantly reduces rural building carbon emissions. The proposed system offers substantial practical value for China’s rural energy transition.

1. Introduction

Energy is the basis for the survival and development of human society. Nowadays, energy shortage and environmental problems are imminent. It has become the consensus of various countries to improve energy utilization efficiency, save energy, and reduce pollutant emissions. According to the report of the International Energy Agency (IEA), the global energy system is under unprecedented pressure due to a confluence of factors: geopolitical conflicts, lagging energy structure transition, and persistently growing global energy demand. The report also indicates that since 2010, the carbon dioxide emissions of the building industry have increased yearly [1], and building energy consumption has accounted for more than 40% of the total global energy consumption. The building industry has great potential for energy conservation and emission reduction. China is a big agricultural country, and the population in rural areas accounts for 41.48% of the total population of the country. However, the construction specifications of rural residential buildings are different, the airtightness of the outer protective structure is poor, and the energy consumption required to maintain the comfortable indoor environment of rural buildings is high. Therefore, residential buildings in rural areas have great potential for energy conservation and emission reduction [2]. Net-zero energy buildings (NZEBs) are one of the key solutions to deal with excessive building energy consumption and large emissions of polluting gases, and it is also the future development trend of the building industry [3,4,5]. Net zero energy consumption refers to a building achieving a dynamic equilibrium where the total energy it consumes is fully offset by the energy generated through its own on-site renewable energy systems over an evaluation period (typically one year). This establishes a self-sustaining balance of using as much as is generated. However, limited by economic conditions in rural areas, the adoption of passive energy saving will result in additional building construction costs for residents, which will have a greater impact on the overall economy of the building system. At the same time, coal-fired energy is still the main energy supply in rural areas of China. There have always been problems of extensive energy management, poor economy, and high levels of polluting gas emissions. Reducing building energy consumption from the energy consumption side alone cannot completely solve the problem in rural areas. As such, it is urgent to adopt a new energy supply method oriented towards the characteristics of rural areas.

The integrated energy system (IES) integrates renewable energy and scientifically dispatches multiple energy sources to achieve coordination and mutual assistance between different energy sources, meet users’ multiple energy needs, and achieve the purpose of improving energy utilization efficiency, energy supply reliability, and safety [6,7]. Rural areas have low population density, large usable roof areas on residential buildings, and abundant natural resources, and have the potential to develop photovoltaic and other renewable energy sources [8,9,10,11]. The inherent biomass resources in rural areas significantly reduce the raw material purchase cost for developing small-scale biomass gas systems. Moreover, Wang et al. [12] found that spatial and temporal variations, especially ground temperature, significantly influence biogas production efficiency in rural China, suggesting a need to integrate environmental factors into IES design. Compared with urban areas, the development of biomass gas systems in rural areas has significant economic advantages [13,14,15,16]. Tan et al. [17] propose a capacity-demand analysis framework for rural biogas power generation under source–load uncertainty, utilizing scenario-based optimization and storage correction models to improve system flexibility and cost-effectiveness. To sum up, the development of net-zero energy building (NZEB)/zero-energy building (ZEB) energy supply systems in rural areas with IESs as energy supply systems has unique environmental and regional advantages. Absolute zero energy, in contrast to net-zero energy, refers to buildings that operate entirely on locally generated and self-sufficient energy systems without drawing any external energy supply. This approach is more technically demanding and has more limited applications. Reasonable planning and operation scheduling of IES can effectively improve the efficiency of energy utilization and transform the traditional energy supply method in rural areas.

At present, the planning, design, and operation scheduling of IESs at the scale of buildings are still the focus of research, and scholars at home and abroad have carried out a series of studies on this. The authors of [18] designed an IES for the unique ecological environment of a mining park and proved the feasibility of the designed scheduling model and the effectiveness of the scheduling through a multi-objective optimization algorithm; however, the model has particularity and is not suitable for ordinary residential energy consumption. The authors of [19] designed a two-level optimization method for a regional IES, considering the time-of-use electricity price and the demand response mechanism, which improved the profit of the system and realized the peak shaving and valley filling of the overall load of the system, but the paper did not provide energy supply. The energy consumption level of the system is constrained. In summary, while current research has achieved certain advancements in IES optimization, most studies remain focused on campus-scale scenarios. Their optimization models predominantly address specific ecological or economic contexts, consequently falling short in addressing the distinct challenges and requirements of energy supply systems.

Aims and Innovations of This Paper

Current research on IES optimization targeting rural buildings remains relatively scarce, with most related studies still at an exploratory stage. Ref. [20] proposed an operation optimization method for rural heating IES in northern China and proved the advantages of the optimized integrated energy heating system in terms of economy and environmental protection through simulation. The heating load is analyzed, and the situation of various load demands of cooling, heating, and power load is not considered. The above references show that the current research focuses on the optimal design and operation scheduling of IES for the energy load at the park level. However, few papers take the energy supply system of rural buildings as the optimization object, and building energy consumption is not considered as constraint in the optimization process. Constrained by economic conditions in rural areas, it is difficult to use passive energy-saving methods to reduce building energy consumption from the source. Therefore, it is necessary to improve energy utilization efficiency from the energy supply side to achieve a net zero energy consumption level for buildings.

Based on the original load data of rural buildings, this paper takes net-zero energy/zero energy as constraint of the building energy supply system and uses a multi-objective optimization algorithm to configure the capacity of key equipment in IES. In the scheduling optimization stage, the system capacity configuration result is used as constraint, and the time-of-use electricity price is considered to optimize the output of the internal combustion generator set in the system to achieve the optimal economic and environmental protection of the system. The main contributions of this paper are as follows:

➀

In order to make full use of the unique advantages of the energy system in rural areas, this paper uses a biomass gas system instead of natural gas as the fuel source, which reduces the cost of gas purchase while meeting the fuel demand of the system, reflecting the unique regional advantages of the rural energy supply system;

➁

In order to solve the current situation of high energy consumption and low efficiency of the energy supply system in rural areas, this paper proposes a rural IES based on combined cooling heating and power (CCHP) microgrid, and compares it with the FES and the sub-supply system to prove the advantages of the proposed IES;

➂

Considering that rural buildings are constrained by economic conditions, it is difficult to change the overall building envelope to reduce building energy consumption from the source side. Therefore, this paper takes ‘net-zero energy consumption’ and ‘zero energy consumption’ as constraints of IES to realize the best economical efficiency and environmental protection of the system.

2. Energy Supply System Structure and Key Equipment Model

2.1. Energy Supply System Structure Comparison

The design of a reasonable energy supply system is of great significance to meet the energy needs of users and improve energy utilization efficiency. Therefore, in response to the promotion of the rural revitalization strategy and the application of low-carbon energy technology, this paper designs an integrated energy supply system. Compared with the traditional SP and the full electrification energy supply system, the advantages of IES in terms of environmental protection and economy are verified.

2.1.1. The Structure of the Integrated Energy System

The structure of the IES that supplies energy for rural buildings is shown in Figure 1. The IES introduces an air source heat pump and a battery into the basis of renewable energy power generation and CCHP and uses biomass gas equipment to provide biomass for the system.

2.1.2. Separated Production and Full Electrification System

At present, SP is still the main method of rural energy supply. The electricity load required by users, including the electricity for air conditioning and refrigeration, comes from the power grid, and heating is provided by the gas boiler. In this method, electricity and heat energy are separated. There is no coupling relationship between various energy sources, and the energy utilization efficiency is low.

To change the current situation of high energy consumption and high carbon emissions in rural energy use and reduce the use of coal, this paper selects an air source heat pump (ASHP) as the energy supply equipment for the user’s cooling and heating load under the fully electrified energy supply system. In the FES, the cooling and heating demands are met by a high-efficiency ASHP, which utilizes electricity purchased from the power grid. Compared with the SP system, where electricity and heat are independently supplied by the grid and a gas boiler, respectively, the FES reduces overall energy losses due to the high COP of ASHPs. This results in improved energy efficiency and lower CO₂ emissions, despite relying entirely on electricity. The parameters related to the power grid are shown in

Table 1. Parameters of grid.

Parameters	Value
Grid generation efficiency	0.35
Grid transmission efficiency	0.92
CO2 emission factor of grid (kg/kWh)	0.968

below.

2.2. Equipment Mathematical Model

2.2.1. The Mathematical Model of Photovoltaic

The output power of the photovoltaic system varies with the solar intensity and ambient temperature; the mathematical model of the photovoltaic is shown in Equation (1). The parameters of the photovoltaic are shown in

Table 2. Parameters of photovoltaic.

Parameters	Value
Unit capacity cost (USD/kW)	1230
Maintenance cost (USD/kWh)	0.056
Service life (years)	22

.

$$P_{\mathrm{PV}}=P_{\mathrm{STC}}I[1+k(T_{\mathrm{PV}}-T_{\mathrm{r}})]/I_{\mathrm{STC}},$$

(1)

where, $I$ is the solar intensity; $P_{\mathrm{STC}}$ is the maximum test power under standard test conditions (the solar intensity $I_{\mathrm{STC}}$ is 1000 W/m², $T_{\mathrm{r}}$ is 25 °C); and $k$ is the power temperature coefficient, which is −0.45%/K. $T_{\mathrm{PV}}$ is the temperature of the photovoltaic power generation module, which can be estimated by testing the ambient temperature.

$$T_{\mathrm{PV}}=T_0+0.03I,$$

(2)

where, $T_0$ is the ambient temperature.

2.2.2. The Mathematical Model of ICE

The characteristic analysis of the internal combustion engine (ICE) includes the thermal efficiency, electrical efficiency, and waste heat recovery of the unit. The relevant parameters of the ICE set are shown in

Table 3. Parameters of internal combustion engine.

Parameters	Value
Unit capacity cost (USD/kW)	812
Maintenance cost (USD/kWh)	0.064
Service life (years)	10

.

$$\left\{\begin{array}{c}{G}_{\mathrm{ICE}}={\displaystyle \frac{P_{\mathrm{ICE}}}{{\eta }_{\mathrm{p}}{\eta }_{\mathrm{m}}}}\\ G_{\mathrm{ICE}}(1-{\eta }_{\mathrm{m}})=Q_{\mathrm{jw}}+Q_{\mathrm{exh}}+Q_{\mathrm{loss}}\\ Q_{\mathrm{re}}=Q_{\mathrm{jw}}{\eta }_{\mathrm{jw}}+Q_{\mathrm{exh}}{\eta }_{\mathrm{exh}}\end{array}\right.$$

(3)

where, $G_{\mathrm{ICE}}$ is the amount of gas consumed by IC; ${\eta }_{\mathrm{p}}$ and ${\eta }_{\mathrm{m}}$ are the electrical and thermal efficiency of ICE, respectively, which are affected by the power load rate (PLR); and $Q_{\mathrm{jw}}$ is the waste heat of jacket water. $Q_{\mathrm{exh}}$ is the waste heat of exhaust gas; $Q_{\mathrm{loss}}$ is the heat loss; $Q_{\mathrm{re}}$ is the recoverable heat; and ${\eta }_{\mathrm{jw}}$ and ${\eta }_{\mathrm{exh}}$ are the efficiency of jacket water heat exchanger and the efficiency of exhaust gas heat exchanger, respectively.

The ICE has different efficiencies and amounts of waste heat produced under different PLR. According to [21], the relationship between the parameters of ICE and PLR of the equipment is shown in the following Equation.

$${\eta }_{\mathrm{m}}=0.005262+1.031\times r- 1.064\times r^2 +0.3198\times r^3,$$

(4)

$${\eta }_p=0.7741\times \exp (0.1846\times r)-0.7741\times \exp (-36.67\times r),$$

(5)

where, $r$ is the PLR of ICE; ${\eta }_{\mathrm{m}}$ is the thermal efficiency of ICE; ${\eta }_p$ is the electrical efficiency of ICE.

The proportional coefficient of the jacket water heat, exhaust gas waste heat, and other heat losses of ICE to the total waste heat of the unit satisfies the following Equation:

$$f_{\mathrm{j}}+f_{\mathrm{e}}+f_{\mathrm{n}}=1,$$

(6)

where, $f_{\mathrm{j}}$, $f_{\mathrm{e}}$, $f_{\mathrm{n}}$ are the waste heat ratio of jacket water, exhaust gas, and other forms of heat loss, respectively. The waste heat ratio of jacket water and exhaust gas are related to the PLR of ICE, which are shown in the following Equations:

$$f_{\mathrm{j}}=0.5606-0.4282\times r+0.8131\times r^2-0.5161\times r^3,$$

(7)

$$f_{\mathrm{e}}=0.3267\times e^{(-{({\displaystyle \frac{r-0.7618}{1.133}})}^2)}+0.06845\times e^{(-{({\displaystyle \frac{r-0.002531}{0.3265}})}^2)},$$

(8)

2.2.3. Battery

During the charging and discharging process of the battery, the state of charge (SOC) of the energy storage in period t is related to the SOC in period t − 1 and the charge and discharge amount of the energy storage in period [t − 1, t] (this paper does not consider the power attenuation of the battery). The SOC of the battery is shown in Equation (9). The battery related parameters are shown in

Table 4. Parameters of battery.

Parameters	Value
Unit capacity cost (USD/kW)	450
Maintenance cost (USD/kWh)	4.6 × 10−4
Service life (years)	10
Charging efficiency	0.9
Discharging efficiency	0.9
Lower limit of the SOC	0.3
Initial SOC	0.5
Power to or from battery	25% of battery capacity

[22].

$$S(t)=\left\{\begin{array}{l}S(t-1)+{\eta }_c{P}_c/C_{ba}, P_c(t)>0\\ S(t-1)+P_d/{\eta }_d/C_{ba},P_d(t)<0\end{array}\right.$$

(9)

where, $S(t)$ and $S(t-1)$ are the SOC of battery at period t and period $t-1$, respectively; ${\eta }_c$ is the charging efficiency of battery; $P_c$ is the battery input power; ${\eta }_d$ is the discharging efficiency of battery; $P_d$ is the battery output power; and $C_{ba}$ is the capacity of battery.

2.2.4. Air Source of Heat Pump

The air source heat pump (ASHP) uses high potential energy (electricity) to flow heat from the low heat source (air) to the high heat source. The parameters of the air-source heat pump are shown in

Table 5. Parameters of air source heat pump.

Parameters	Value
Unit capacity cost (USD/kW)	500
Maintenance cost (USD/kWh)	0.0044
Service life (years)	20
COP	3

below. The characteristics of the ASHP can be expressed as the proportional relationship between the input power and the cooling (heating) capacity, as shown in the following Equation:

$$Q_{\mathrm{HP}}^{\mathrm{out}}=P_{\mathrm{HP}}^{\mathrm{in}}CO{P}_{\mathrm{HP}},$$

(10)

where, $Q_{\mathrm{HP}}^{\mathrm{out}}$ is the output cooling/heating of ASHP; $P_{\mathrm{HP}}^{\mathrm{in}}$ is the input electricity of ASHP; and $CO{P}_{\mathrm{HP}}$ is the coefficient of performance (COP) of ASHP.

2.2.5. Biomass

Rural areas are rich in solid biomass waste, and the cost of raw materials is low, which is suitable for the development of small- and medium-scale biomass gas technology. The average annual purchase cost of biomass gas equipment is shown in Equation (11) [23]. The parameters related to biomass gas equipment are shown in

Table 6. Parameters of biomass gasification.

Parameters	Value
Interest rate (%)	12
Calorific value of biogas (kWh/m3)	6.87
Maintenance cost (USD/kWh)	1.06
Service life (years)	30
CO2 emission factor (kg/kWh)	0.196
Unit preparation cost (USD/kWh)	0.034

below.

$$C_{\mathrm{Biomass}}=1600\times m_{\mathrm{Biomass}}^{0.67}\phi \mathrm{CRF},$$

(11)

where, $m_{\mathrm{Biomass}}$ is the flow rate of combustible gas output by biomass gas equipment per hour; $\phi$ is the maintenance cost; and CRF is the capital recovery factor, which is shown in Equation (12) [24].

$$\mathrm{CRF}={\displaystyle \frac{i{(1+i)}^n}{{(1+i)}^n-1}},$$

(12)

where, $i$ is the interest rate and n is the service life.

2.3. Load Balance Relationship

The IES realizes the load balance between the energy supply end and the residential side through the coupling of multiple devices. The energy balance relationship is shown in Equations (13)–(15):

$$P_{\mathrm{PV}}+P_{\mathrm{ICE}}+P_{\mathrm{grid}}=P_{\mathrm{HP}}^{\mathrm{in}}+P_{\mathrm{Battery}}+P_{\mathrm{load}},$$

(13)

$$Q_{\mathrm{HP}}^{\mathrm{out}}+Q_{\mathrm{AC}}=Q_{\mathrm{cool}},$$

(14)

$$Q_{\mathrm{HP}}^{\mathrm{out}}+Q_{\mathrm{Boiler}}+Q_{\mathrm{re}}=Q_{\mathrm{heat}},$$

(15)

where, $Q_{\mathrm{cool}}$, $Q_{\mathrm{heat}}$, $P_{\mathrm{load}}$ are the cooling, heating, and electricity load of rural buildings, respectively; $P_{\mathrm{Battery}}$ is the battery charge/discharge power (charging power is positive, discharging power is negative); $P_{\mathrm{HP}}^{\mathrm{in}}$ is the power consumption of ASHP; $Q_{\mathrm{HP}}^{\mathrm{out}}$ is the cooling/heat output from ASHP; $P_{\mathrm{grid}}$ is the purchase/sale electricity from/to the grid by IES (Purchasing electricity is positive, selling electricity is negative); $Q_{\mathrm{AC}}$ is the cooling output of the absorption chiller; and $Q_{\mathrm{Boiler}}$ is the heating output of gas boiler.

3. Capacity Configuration and Operation Scheduling Optimization

3.1. Capacity Configuration Strategy of Net-Zero Energy/Zero Energy

The integrated energy system adopts the ‘grid-connection’ operation mode under the constraint of net-zero energy consumption. The net-zero energy supply flowchart of IES is shown in Figure 2. ‘Following electrical load’ (FES) operation strategy is adopted at capacity configuration stage. First, the cooling and heating load provided by the air source heat pump is calculated according to the electric cooling ratio, and the electric load required by the ASHP is obtained, which can meet the requirements of the building through renewable energy power generation, battery discharge, ICE power generation, and grid power purchase. On the basis of meeting the electrical load demand, the waste heat generated by the ICE and the heat generated by the gas boiler meet the cooling and heating load requirements of the building, and the insufficient cooling and heating load is supplemented by the gas boiler. The net-zero energy constraint is shown in Equation (16):

$$P_{\mathrm{sale},\mathrm{grid}}^{\mathrm{y}}\ge P_{\mathrm{pur},\mathrm{grid}}^{\mathrm{y}},$$

(16)

where, $P_{\mathrm{sale},\mathrm{grid}}^{\mathrm{y}}$ is the electricity sold by the system to the grid and $P_{\mathrm{pur},\mathrm{grid}}^{\mathrm{y}}$ is the electricity purchased from the grid.

To meet the energy demand of remote rural areas that cannot be directly covered by grid, this paper proposes a comprehensive energy supply system under the condition of zero energy consumption. The energy supply equipment can meet the load demand of users. The capacity configuration of IES under the zero-energy consumption constraint is more stringent than that under the net-zero energy consumption constraint. The optimization variables in the capacity configuration stage of the system are the same as those under the net-zero energy consumption constraint. The flow chart of the optimization phase of building zero-energy capacity configuration is shown in Figure 3. The multi-objective optimization model of capacity configuration based on the energy balance relationship of IES takes the capacity of photovoltaic generator $C_{PV}$, the capacity of battery $C_{battery}$, the capacity of ICE $C_{ice}$, the capacity of ASHP $C_{ashp}$, the capacity of biomass equipment $C_{gas}$, and electric cooling ratio Re as optimization variables. Based on the multi-objective optimization algorithm, the annual cost saving rate (ACSR) and CO₂ emission reduction rate (CO₂ERR) of IES are optimized.

The Re is defined as the proportion of the building’s total cooling and heating load that is supplied by electric-driven equipment, such as the air source heat pump (ASHP), instead of by thermal-driven devices (e.g., absorption chillers powered by internal combustion engine waste heat). Mathematically, it can be expressed as:

$$R_e={\displaystyle \frac{Q_{\mathrm{ASHP}}}{Q_{\mathrm{Total}}}},$$

(17)

where $Q_{\mathrm{ASHP}}$ is the cooling/heating output provided by the ASHP and $Q_{\mathrm{Total}}$ is the total cooling and heating demand of the building.

The electric cooling ratio is a key decision variable in the capacity configuration process because it directly influences how cooling and heating loads are shared between electrical and thermal subsystems. A higher Re indicates more reliance on electric-driven systems (ASHP), which may increase electricity demand and grid dependency but also offer higher energy efficiency (due to high COP). In contrast, a lower Re reflects greater use of waste heat from the internal combustion engine, which reduces electricity consumption but may require more gas input and increase carbon emissions. Therefore, optimizing Re helps to balance system cost, fuel consumption, CO₂ emissions, and operational flexibility.

To solve this multi-objective problem, we adopt the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is well-suited for Pareto-based optimization. The main steps of the algorithm include:

• Initialization: Randomly generate an initial population with N individuals (solutions), each encoded with values for the six decision variables.
• Fitness Evaluation: Evaluate each individual using the two objective functions (ACSR and CO₂ERR).
• Non-dominated Sorting: Rank the population into Pareto fronts based on dominance relations.
• Crowding Distance Calculation: Compute crowding distances to preserve diversity within Pareto fronts.
• Selection, Crossover, and Mutation: Apply binary tournament selection, simulated binary crossover (SBX), and polynomial mutation to generate offspring.
• Environmental Selection: Combine parent and offspring populations and select the top N individuals for the next generation.

The algorithm runs for 200 generations with a population size of 100. The crossover probability is set to 0.9 and the mutation probability is 1/6. These parameters are selected based on empirical tests and relevant literature.

3.2. Typical Daily Load Operation Scheduling Optimization

The equipment capacity results obtained in the upper-level capacity configuration stage in the operation scheduling optimization stage are used as constraints. On the basis of the typical daily load data in summer and winter, the hourly PLR of IES is used as the optimization variable to meet the cooling and heating load of rural demand. Compared with SP and FES, the daily operating cost saving rate and the daily carbon dioxide emission reduction rate are used as the objective functions to achieve the optimal economy and environmental protection of the system. The time-of-use electricity price used by the optimization program is shown in

Table 7. Time-of-use electricity prices of grid.

Electricity Price (USD/kWh)
Peak rice (11:00~14:00, 18:00~23:00)	Intermediate price (7:00~11:00, 14:00~18:00)	Valley price (23:00~7:00)
0.168	0.108	0.057

below, and the electricity price of the system to the grid is 0.121USD/kWh.

3.3. Objective Function

Reasonable evaluation of IES capacity configuration and operation optimization results is of great significance to ensure the effective operation of the system. According to the optimization results of the capacity configuration stage of IES of rural buildings, this paper selects the annual cost saving rate (ACSR) and CO₂ emission reduction rate (CO₂ERR) as the objective functions, which are shown below [25].

$$F_{\mathrm{ACSR}}={\displaystyle \frac{C^{\mathrm{y}}-C_{\mathrm{IES}}^{\mathrm{y}}}{C^{\mathrm{y}}}},$$

(18)

where, $C^{\mathrm{y}}$ is the annual cost of SP or FES; $C_{\mathrm{IES}}^{\mathrm{y}}$ is the annal cost of IES (the annal cost includes annual purchase cost, maintenance cost, and operation energy cost); and $F_{\mathrm{ACSR}}$ is the annual cost saving rate.

$$F_{{\mathrm{CO}}_2\mathrm{ERR}}^{\mathrm{y}}={\displaystyle \frac{{\mathrm{CO}}_2{\mathrm{E}}^{\mathrm{y}}-{\mathrm{CO}}_2{\mathrm{E}}_{\mathrm{IES}}^{\mathrm{y}}}{{\mathrm{CO}}_2{\mathrm{E}}^{\mathrm{y}}}},$$

(19)

where, ${\mathrm{CO}}_2{\mathrm{E}}_{SP}^{\mathrm{y}}$ is the total CO₂ emission of SP or FES; ${\mathrm{CO}}_2{\mathrm{E}}_{\mathrm{IES}}^{\mathrm{y}}$ is the total CO₂ emission of IES; and $F_{{\mathrm{CO}}_2\mathrm{ERR}}^{\mathrm{y}}$ is the annual CO₂ emission reduction rate.

The operation optimization level takes the typical daily operation cost saving rate (DOCSR) and CO₂ emission reduction rate as the objective function, as shown in Equations (20) and (21).

$$F_{\mathrm{OCSR}}^{\mathrm{d}}={\displaystyle \frac{C^{\mathrm{d}}-C_{\mathrm{IES}}^{\mathrm{d}}}{C^{\mathrm{d}}}},$$

(20)

where, $C^{\mathrm{d}}$ is the typical daily energy operation cost of SP or FES; $C_{\mathrm{IES}}^{\mathrm{d}}$ is the typical daily energy operation cost of IES; and $F_{\mathrm{OCSR}}^{\mathrm{d}}$ is the daily operation cost saving rate.

$$F_{{\mathrm{CO}}_2\mathrm{ERR}}^{\mathrm{d}}={\displaystyle \frac{{\mathrm{CO}}_2{E}^{\mathrm{d}}-{\mathrm{CO}}_2{E}_{\mathrm{IES}}^{\mathrm{d}}}{{\mathrm{CO}}_2{E}^{\mathrm{d}}}},$$

(21)

where, ${\mathrm{CO}}_2{E}^{\mathrm{d}}$ is the typical daily CO₂ emission of SP or FES; ${\mathrm{CO}}_2{E}_{\mathrm{IES}}^{\mathrm{d}}$ is the typical daily CO₂ emission of IES; and $F_{{\mathrm{CO}}_2\mathrm{ERR}}^{\mathrm{d}}$ is the daily CO₂ emission reduction rate.

4. Building Load Simulation and Operation Optimization

4.1. Load Simulation

This paper builds a rural building model based on SketchUp simulation software, as shown in Figure 4 below. The building is divided into six thermal zones, which are planned as two living rooms, two bedrooms, one kitchen, and one dining room. Different thermal zones consider different thermal requirements, personnel activities, and equipment work schedule. On the basis of the SketchUp building model, this paper uses EnergyPlus to simulate the cooling, heating, and electricity load data of a single rural building [26]. In this optimization process, a small building group consisting of 34 rural buildings is considered, and the cooling, heating, and electricity load of the building group is used as the optimization database. The building envelope and related parameters are shown in

Table 8. Peripheral envelope parameters.

Envelope Type	Thickness (mm)	Heat Transfer Coefficient (W/m2k)
External wall	270	1.058
Roof	240	1.068
Floor	225	1.101
External window	\	1.1

below. The influence of building materials on energy demand is acknowledged. However, this study assumes typical rural envelope parameters as fixed, based on national standards, to focus solely on analyzing the performance of the Integrated Energy System (IES).

After the above parameter design, this paper takes ‘hour’ as the time scale and uses typical meteorological year (TMY) weather data of a northern Chinese city to simulate the annual cooling, heating, and electricity loads of rural buildings.

Although the building model is not based on a specific real house, it represents a typical rural residential structure. All envelope parameters, equipment usage, and occupancy schedules are set to fixed values in accordance with Chinese national building standards and relevant literature.

The activities of personnel inside the building, the usage of equipment, the time distribution of lighting switches, and the indoor ventilation of the building meet the national standards. After the above parameter design, this paper takes ‘hour’ as the time scale and takes the weather data of a city in northern China as the basis of the simulation data to obtain the annual cooling, heating, and electricity load data of rural buildings, as shown in Figure 5 below.

4.2. Analysis of Capacity Configuration of IES

The control strategy and key parameters of the equipment of the rural IES under the net-zero energy consumption and zero energy consumption constraints have been given in the second part of this paper. The capacity configuration and operation optimization of the system are completed by the NSGA-II program under the MATLAB software (Version: R2021a). The ACSR and the annual CO₂ERR are shown in

Table 9. Optimization Results of Capacity Configuration.

Energy Constraint	Contrast System	Annual Operation Cost (USD)	Annual Operation Cost of IES (USD)	ACSR	Annual CO2 Emission (kg)	Annual CO2 Emission of IES (kg)	CO2ERR
Net-zero energy	SP	1.29 × 105	1.17 × 105	9.12%	1.69 × 106	4.90 × 105	70.97%
	FES	1.25 × 105	1.14 × 105	8.57%	2.07 × 106	5.56 × 105	73.2%
Zero energy	SP	1.29 × 105	1.83 × 105	−106.94%	1.69 × 106	3.49 × 105	83.19%
	FES	1.25 × 105	1.74 × 105	−39.76%	2.07 × 106	3.68 × 105	82.24%

, and the optimization results of the key equipment of the system in the capacity configuration stage are shown in

Table 10. Capacity configuration results of key equipment.

Energy Constraint	Contrast System	PV (kW)	Battery (kWh)	ICE (kW)	ASHP (kW)	Biomass (Number)	Electric Cooling Ratio
Net-zero energy	SP	520	52	88	41	136	31%
	FES	464	47	55	14	155	10%
Zero energy	SP	404	325	120	16	167	11%
	FES	333	250	133	34	172	13%

.

It can be seen from Table 10 that when the system is under the constraint of net-zero energy consumption, the IES is equipped with large-capacity PV, which can sell electricity to the grid while meeting the needs of rural users. While reducing the overall investment cost of the system, it improves the overall benefits to rural residents, reduces the load pressure on the power grid, absorbs excess carbon emissions for the power grid, and improves the overall economy and environmental friendliness of the system. When the system is operating under zero energy consumption constraints, in order to meet the user’s cooling, heating, and electric load requirements, the IES is equipped with a large-capacity battery and ICE. As the main energy supply equipment of the system, IES provides users with thermal power, while the battery is used as the energy storage device of the system. Through reasonable charging and discharging, the occurrence of power shortage on the user side is reduced, and the energy supply reliability of the system is improved. To meet the biogas fuel requirements of IES, the number of biomass equipment required by the system is higher than in the case of net-zero energy consumption. Under the constraint of zero energy consumption, since the system cannot interact with the power grid, the flexibility of power dispatch is poor, and the overall electric cooling ratio of the system is at a low level.

From a comprehensive analysis of Table 9 and Table 10, it can be seen that the system under the constraint of net-zero energy consumption is superior to SP and FES in terms of annual cost and annual CO₂ emission, and the annual CO₂ emission reduction effect is significant. It can be seen that IES has great emission reduction advantages, and the system is suitable for rural areas that can interact with the grid. Under the constraint of zero energy consumption, IES is constrained by geographical location and cannot be directly connected to the power grid; thus, the overall capacity configuration of the equipment is relatively large. The capacity of the corresponding equipment under the constraint of net-zero energy consumption, the total annual cost of the system is higher than that of SP and FES, but the CO₂ emission reduction effect of the system is better than that of net-zero energy consumption, which further improves the environmental protection of the system.

4.3. Typical Daily Operation Scheduling Optimization of IES

4.3.1. Typical Daily Operation Scheduling Optimization of Net-Zero Energy IES

Under the constraint of net-zero energy consumption, the daily OCSR and daily CO₂ERR of IES compared to SP and FES are shown in

Table 11. Operating costs on a typical summer day under net-zero energy consumption.

Contrast System	Daily Operation Cost (USD)	Daily Operation Cost of IES (USD)	Daily OCSR
SP	311	−138	144.47%
FES	293	−121	141.24%

,

Table 12. Operating costs on a typical winter day under net-zero energy consumption.

Contrast System	Daily Operation Cost (USD)	Daily Operation Cost of IES (USD)	Daily OCSR
SP	319	70	78.19%
FES	313	81	73.98%

,

Table 13. CO₂ emissions on a typical summer day under net-zero energy consumption.

Contrast System	Daily CO2 Emission (kg)	Daily CO2 Emission of IES (USD)	Daily CO2ERR
SP	6.58 × 103	1.52 × 103	76.89%
FES	6.21 × 103	1.59 × 103	63.93%

and

Table 14. CO₂ emissions on a typical winter day under net-zero energy consumption.

Contrast System	Daily CO2 Emission (kg)	Daily CO2 Emission of IES (USD)	Daily CO2ERR
SP	4.58 × 103	1.65 × 103	63.93%
FES	6.96 × 103	1.84 × 103	73.54%

below. Taking the optimization results compared with SP as an example, under the condition of net-zero energy consumption, the cooling, heating, and electric loads of rural buildings and the output of key equipment are shown in Figure 6 and Figure 7. It can be seen from the figure that the operation of IES has obvious time distribution characteristics. During the period 0–8 o’clock, the user’s cooling, heating, and electrical loads are at a relatively low level. The electrical load is provided by IES and the waste heat of IES is used to provide cooling and heating loads. The PLR of IES changes in the time period when the heating load demand is large. During the period 9–14 o’clock, the electrical load of the system is mainly satisfied by PV, and the PLR of IES is low. PV consumes excess electricity by selling electricity and charging the battery. During the period from 15 to 18 o’clock, affected by the temperature, the demand for cooling load in summer is large, and the demand for heating load in winter is small. The PLR of IES gradually increases in summer, and the PLR of IES in winter remains stable. During the period from 19 to 22 o’clock, due to the influence of the living habits of the residents, the electric load reaches the peak period of the whole day, the IES works at full load, and the battery is discharged at the same time. During the period from 23 to 24 o’clock, the cooling, heating, and electric loads all decreased by varying degrees, the PLR of IES decreased accordingly, and the cooling and heating energy provided by ASHP decreased accordingly.

Comparing the operation of equipment on typical days in winter and summer, it can be seen that due to the influence of temperature and solar in winter, the power generation of PV is limited, and the overall PLR of IES remains at a high level. Sales of electricity to the grid are reduced in winter compared to typical days in summer.

It is noted that under the zero-energy constraint, the IES operates in an off-grid mode and cannot sell excess electricity. As a result, more internal generation and storage are required, which increases operational costs. In such cases, the DOCSR can be negative, indicating that the IES is more expensive to operate than the reference systems. This highlights the trade-off between energy independence and economic performance.

4.3.2. Typical Daily Operation Scheduling Optimization of Zero-Energy Integrated Energy System

Under the condition of zero energy consumption, the cooling, heating, and electrical loads of rural buildings and the output of key equipment are shown in Figure 8 and Figure 9. It can be seen from the figures that the PLR of IES is higher from 18 to 22 o’clock, and the minimum PLR of IES is maintained at other time periods. During the period from 0 to 6 o’clock, the user’s cooling, heating, and electrical loads are at a low level. The electrical load is the same as the net-zero energy consumption situation. It is mainly satisfied by IES, and the cooling and heating load is provided by the waste heat of IES. During the period of 7–8 o’clock, in order to meet the user’s electrical load demand, IES starts to rise following the change of electric load. During the period from 9 to 17:00 o’clock, the electric load of the system is mainly satisfied by PV, the PLR of IES is low, and the remaining power of PV cannot be sold to the power grid, so the battery charging capacity is high. During the period from 18 to 22 o’clock, the PLR of IES increases with the increase of the electric load, and the insufficient electric energy of the system is supplemented by the battery discharge. From 23 to 24 o’clock, the IES burns biogas to generate waste heat to provide users with cooling and heating loads, and the excess electricity is charged to the battery.

Compared with IES under the constraint of net-zero energy consumption, the power generation of PV is reduced in winter, and the system cannot interact with the power grid, so the battery charge and discharge volume is large, and the PLR of ICE only changes greatly at night. Since the system is in an ‘off-grid’ operation state, the flexibility of the system’s electrical energy is poor. In order to reduce the electric load pressure of the system, the overall electric cooling ratio is small, and the user’s cooling and heating loads are mainly provided by the waste heat of ICE.

From the data in

Table 15. Operating costs on a typical summer day under zero energy consumption.

Contrast System	Daily Operation Cost (USD)	Daily Operation Cost of IES (USD)	Daily OCSR
SP	311	178	42.8%
FES	293	191	35.04%

,

Table 16. Operating costs on a typical winter day under zero energy consumption.

Contrast system	Daily Operation Cost (USD)	Daily Operation Cost of IES (USD)	Daily OCSR
SP	319	188	40.99%
FES	313	201	35.91%

,

Table 17. CO₂ emissions on a typical summer day under zero energy consumption.

Contrast System	Daily CO2 Emission (kg)	Daily CO2 Emission of IES (USD)	Daily CO2ERR
SP	6.58 × 103	1.03 × 103	84.41%
FES	6.21 × 103	1.1 × 103	82.28%

and

Table 18. CO₂ emissions on a typical winter day under zero energy consumption.

Contrast System	Daily CO2 Emission (kg)	Daily CO2 Emission of IES (USD)	Daily CO2ERR
SP	6.58 × 103	1.09 × 103	76.32%
FES	6.21 × 103	1.16 × 103	83.38%

, it can be seen that compared with SP and FES, IES under the condition of zero energy consumption shows good economic efficiency and environmental protection under the condition of typical daily operation. The cost of IES under the constraint of zero energy consumption is much higher than that of other systems in the initial investment stage, but the system has low operating energy cost and low CO₂ emission during the actual operation process, which will show a significant advantage in the future rural energy supply system.

The analysis results demonstrate that seasonal temperature variations have a significant impact on the operational performance of the IES. Specifically, during summer, higher cooling loads coincide with increased photovoltaic (PV) output due to stronger solar irradiance. This enables the IES to utilize more renewable energy and battery storage, resulting in lower daily operational costs and higher CO₂ emission reduction compared to conventional systems. In contrast, during winter, reduced PV generation and increased heating demand lead to a higher reliance on internal combustion engines (ICE) and biomass systems to meet energy needs. Consequently, the operational cost rises in winter, despite the system still achieving substantial CO₂ emission reductions. This seasonal variation highlights the important role of temperature-related load profiles in determining both the economic and environmental performance of the IES.

5. Conclusions

Improving energy utilization efficiency and reducing carbon dioxide emissions are key priorities in the development of China’s energy industry. Promoting the application of IES provides a new pathway for transforming rural energy supply modes. In this paper, a CCHP-based rural IES is constructed by integrating locally available biomass biogas and PV resources along with battery storage. An optimization model is established to configure the capacity of key components under two energy consumption constraints: net-zero energy and zero energy. Typical daily cooling, heating, and power loads for rural buildings in both summer and winter are used to develop an operation scheduling strategy based on hourly part-load ratio (PLR) optimization. Simulation results show that under the net-zero energy constraint, the IES achieves an annual cost saving rate of 8.57–9.12% and a CO₂ emission reduction rate of 70.97–73.2% compared to conventional SP and FES systems. Under the zero-energy constraint, although the system incurs higher operational costs, it achieves even greater emission reductions, over 82%, making it more suitable for off-grid or remote areas with limited access to external power sources.

In future work, the model can be further enhanced by incorporating uncertainty analysis, real-time pricing mechanisms, and demand response strategies to improve its flexibility and robustness. Additionally, integrating multi-agent cooperative scheduling for community-level energy sharing and exploring hybrid storage systems could enhance the resilience and economic viability of rural IES applications.