Optimal scheduling of a township integrated-energy system using the adjustable heat-electricity ratio model

0 Introduction

Under the rural revitalization strategy,the proportion of non-commodity energy consumption in rural areas has continued to decline [1].With the increase in energy demand in rural areas,carbon emissions in rural areas are also increasing.The primary reason for this is that township residents are more scattered,use rough energy,and have low energy utilization efficiency [2].The development of the township economy is expected to inevitably drive the pace of rural electrification,informatization,and industrialization,and also trigger the blowout growth of rural multi-energy demand.In rural areas of China,the development potential of new energy resources is considerable,and distributed photovoltaic (PV) and wind turbines can provide more than 1.5 billion and 100 million kW,respectively,for development and utilization.Moreover,the number of biomass resources is significant.These energy sources can satisfy the future incremental load of rural areas and also clean energy.This can effectively improve the energy self-sufficiency rate in rural areas and ensure the security of the energy supply.Therefore,exploiting clean energy and improving energy utilization efficiency in towns and villages are of great significance for promoting the modernization of towns and villages and achieving “carbon peak and carbon neutrality” goals [3].

The integrated energy system coordinates distributed generation and natural gas in the form of a distributed energy network [4],which changes the situation of traditional energy systems to single and separate.It offers significant advantages in improving energy utilization efficiency in rural areas and promoting the transformation and upgradation of energy structures [5].When the integrated energy system was proposed,relevant studies focused on the selection of equipment according to regional load characteristics and the optimization of regional energy allocation [6].However,because of the singleload characteristics of the region,the economy is not fully reflected in the optimization results,and the multi-energy coordination characteristics of the integrated energy system cannot be demonstrated [7].

To exploit the optimization results of integratedenergy systems and improve energy efficiency and energy-saving effects,scholars have studied the coupling relationship among multiple energy sources,focusing on the optimization objectives of economic and environmental benefits.In [8],the coordinated optimization of the capacity allocation of a multi-region combined cooling,heat,and power (CCHP) system was performed based on a mixedinteger linear programming model.Another study [9]used a mixed-integer programming model to optimize the scheduling of an integrated energy system in a park,considering both economic and environmental objectives.Previous studies in [10,11] have discussed the optimal planning and operation strategies for CCHP systems in terms of both economic and environmental benefits.In [12],a decomposition-iterative method was proposed for the existing problems of the electricity-heat coupling integratedenergy system model.

In terms of model solving,the operation optimization of the integrated energy system involves numerous variables and complex working conditions,and the operation optimization model of the integrated energy system is often a multi-objective nonlinear model.Although conventional algorithms can be used easily to solve the operation optimization target,the calculation time is slow,and it easily falls into the local optimal state.Therefore,intelligent algorithms are often used for solving [13,14].A previous study in [15] used a genetic algorithm to solve the problem of hybrid electric and hydrogen energy storage planning to achieve clean energy consumption.Another study in [16] proposed an optimization model of an electric hot gas hydrogen integrated energy system taking demand response,which was solved using an improved sparrow search algorithm.Owing to its fast convergence speed and high stability,particle swarm optimization (PSO) has been widely used in the operation of integrated energy systems[17,18].In [19],PSO was used to solve the game planning model of the integrated energy system of an isolated island microgrid.In [20],a comprehensive energy scheduling model in the electric-heat-gas region was established and optimized using an optimization algorithm.

Currently,the primary energy sources in integrated energy systems are natural gas and renewable energy sources such as wind,PV,and geothermal energy [21].Natural gas reserves in China are limited;therefore,excessive reliance on natural gas will limit the further development of integrated energy systems and the achievement of “carbon peaking and carbon neutrality” in China.Therefore,clean energy sources with large reserves that have not yet been fully utilized should be sought.The utilization of biomass energy is a key direction for energy development in townships [22].In January 2021,“The CPC Central Committee’s Proposal on the Formulation of the 14th Five-Year Plan (2021-2025) for National Economic and Social Development and the Long-Range Objectives Through the Year 2035” indicated that rural resource energy should be upgraded and rural biomass energy be developed.Combining biomass energy with combined heat and power(CHP) systems and constructing a biomass-integrated energy system (BIES) can improve energy conversion efficiency,reduce fossil energy use,and increase economic returns [23].

Biomass energy can be generated in two main ways [24,25].The first involves burning biomass directly to generate electricity,which is simple and direct.However,owing to the low quality of biofuels,their combustion efficiency is low,and a certain degree of pollution is generated [26].Another method is decomposition,which utilizes methaneor hydrogen-producing bacteria to decompose the organic matter in biomass [27],produce combustible gas,transfer it through the pipe network,and generate electricity.This method improves the efficiency of energy utilization and is more economical and environmentally friendly.An increasing number of studies have been conducted based on this method.

A previous study in [28] considered the economic and environmental benefits and analyzed the cost of forest-based biomass CHP.Reference [29] considered the application of solar energy and biomass in rural areas and established a rural household-based distributed energy supply system.Reference [30] constructed a framework for an integrated energy system based on biomass energy and established an operational optimization model in terms of both economics and energy utilization efficiency.Reference [31] evaluated the feasibility of using biomass to satisfy local energy demands and established a BIES incorporating biogas power generation.Reference [32] established control models for wind,PV,and biogas generators to realize the comprehensive utilization of multiple energy sources.

The above research results have laid a foundation for follow-up work and verified that energy systems and biomass energy offer good economic and environmental benefits.However,two important problems still need to be resolved.First,in the study of biomass cogeneration operation optimization,the thermoelectric ratio is usually fixed;in this mode,the supply exceeds demand,resulting in energy wastage.Second,the grade of biomass energy is low and there is insufficient combustion,which reduces the efficiency of energy utilization.

Therefore,supplementary combustion equipment must be added to biomass cogeneration to increase the combustion efficiency by supplementing combustible gas,thereby adjusting the thermoelectric ratio of the system and reducing energy waste.This study makes the following contributions and innovations.

1) A biomass-integrated energy system including a sewage source heat pump aimed at the lowest economic cost and highest utilization rate of clean energy was constructed,and the cascade utilization of energy was used.

2) The cooperative operation strategy of a sewage source heat pump and cogeneration was studied.Consequently,an operation framework and operation optimization ideas for a biomass-integrated energy system were proposed.

3) The cooperative operation of wind,light,energy storage,and water were optimized to meet the power and heat demands of the system,respond to market mechanisms such as time of use (TOU) price,and consider the economic and environmental benefits of the system.

The remainder of this paper is organized as follows.In Section 1,a typical architecture of an integrated biomass energy system is proposed,and the equipment control strategy based on the characteristics of the wastewater reuse process is discussed.In Section 2,the principle of thermoelectric ratio regulation is presented and the objectives,constraints,and algorithms of the optimization model are discussed.In Section 3,a simulation analysis is presented and the simulation results are compared to verify the economy and applicability of the proposed model.Finally,Section 4 concludes the study.

1 Operational architecture and strategies for township integrated-energy systems

1.1 Architecture for township integrated-energy systems

This study examined an integrated-energy system comprising three subsystems∶ biomass treatment,electricity,and heat.The biomass treatment subsystem used anaerobic treatment to convert waste and sludge in the sedimentation tank into biogas,which was then used for biomass CHP.Wastewater from the feedstock was discharged to a wastewater treatment point where a wastewater source heat pump was installed at the outlet to utilize the low-quality heat from the treatment point to satisfy the heat load on the consumer side.Wind,solar,and biogas were used as the energy sources for power generation in BIES.The impact of the volatility of renewable energy sources was mitigated by incorporating energy storage devices into the BIES to interact and coordinate with the grid to satisfy the electrical load within the system.The heat system comprised a biomass CHP system,electric boilers,and wastewater source heat pumps.Biomass CHP and wastewater source heat pump systems generated heat to satisfy most of the heat load in the system and activated the electric boiler in case of insufficient heat supply.The operating framework of the system is illustrated in Fig.1.

Fig.1 Operational framework of the township BIES

As shown in Fig.1,in addition to the distributed power supply and energy storage equipment,the BIES contained a biomass treatment system and a sewage source heat pump.Biomass cogeneration converted the energy contained in biogas into electricity and heat.The sewage source heat pump used a low level of heat energy in the wastewater after biomass treatment to supply heat and satisfy the heat load.

Owing to the fluctuations in wind and PV power generation,when the output level of the wind and PV decreases,if the heat pump and biomass cogeneration cannot respond in time,the system will have an imbalance between supply and demand.In addition,because of the stable supply of biogas in biomass treatment,natural gas must be supplemented when the system load demand is excessively high.Therefore,it is necessary to consider the system load and power generation,formulate optimization strategies,and determine the overall operation scheduling scheme of the system.

1.2 Optimization strategy for system operation

In system operation,in contrast to the uncontrollable output power of wind turbines and PV equipment,biomass CHP and wastewater source heat pumps can actively adjust the input power such that the equipment output power can be controlled in the range of 30%-100%,thus improving the flexibility of the system [33].The energy use efficiency of the system was further improved by incorporating an afterburning device into the biomass CHP system,thus adjusting the heat-electricity ratio of the biomass CHP system and improving its economic efficiency.A logic diagram of the system optimization process is shown in Fig.2.

Fig.2 Logic diagram of the township BIES optimization process

As evident,during system optimization,the biomass treatment system utilized anaerobic treatment to convert the waste and sludge into biogas and wastewater,respectively.These byproducts were then used as raw materials in the CHP units and wastewater source heat pumps.If the output power of the biomass CHP and wastewater source heat pump was insufficient to satisfy the heat load,natural gas was supplemented through afterburning devices.If the heat load was still not satisfied,an electric boiler was prepared.When the operation plan for the biomass processing system and electric boiler was established,the total electricity demand curve was determined based on the system load.Subsequently,an output plan for power generation equipment such as wind turbines,PV systems,and energy storage was arranged accordingly.Finally,the system operation plan was optimized based on the time-division purchase and sale prices of the power grid,and the overall operation optimization plan was issued to each equipment unit.

2 Operation optimization model of township integrated-energy system

2.1 Adjustment principle of the heat-electricity ratio

The biomass CHP unit comprised a combustion engine system and a waste heat boiler system that could convert the input biogas into electrical and heat energy [34].The waste heat boiler utilized the waste heat generated during the electricity production process as additional heat energy,thereby improving the energy utilization efficiency.The heat-electricity ratio is defined as the ratio of the actual heat power output to the electrical power output of the biomass CHP unit as follows∶

where RCHP is the biomass CHP heat-electricity ratio at time t,is the biomass CHP output heat power at time t,andis the biomass CHP output electrical power at time t.

Waste heat boilers can be categorized into two types based on whether combustion equipment is incorporated∶waste heat boilers with and without afterburning devices.When a waste heat boiler with afterburning devices is in operation,natural gas is injected into the waste gas recovery flue and mixed with the waste gas for combustion;the excess oxygen in the exhaust of the combustion engine is used as the oxidizer of the fuel,and the internal space of the inlet flue of the boiler is used as the afterburning chamber.The waste heat from the exhaust of the combustion engine and the heat introduced into the boiler by the afterburning devices together produce steam or hot water after heat exchange with the furnace water,thus supplying heat to the CHP.

2.2 Objective function

The objective of BIES operation optimization was to reduce the operating cost of the system and consume as much clean energy as possible [35].Therefore,the objective function of the system operation optimization was derived as follows∶

1) Objective function I∶ Minimization of operating costs

The operating costs of the system mainly include the power purchase,gas purchase,biomass fuel,equipment operation and maintenance,and depreciation costs.The objective function is expressed as follows∶

where CBIES is the operating cost of the system,CE is the power purchase cost of the system,CG is the gas purchase cost,CB is the biomass fuel cost,CM is the operation and maintenance cost of the system,and CD is the depreciation cost.They are expressed as follows∶

where is the purchase price,is the purchasing power,Δt is the time interval,is the purchase price of gas,is the input power of natural gas,and Qg is the low calorific value of natural gas,which is 9.97 (kW·h)/m3.Further,is the unit biomass energy cost,the local biomass price is considered,is the input power of natural gas,Qb is the low-calorific value of biomass,N is the device collection,Cuc is the device operation and maintenance cost per unit power,Pi is the device output power during period t,am,bm,and cm are the cost coefficients of the energy storage operation,andis the energy storage operation power for period t.

2) Objective function II∶ Maximizing clean energy utilization

BIES integrates biomass,solar,wind,and other clean energy sources.To improve the energy self-sufficiency rate of the park and ensure the maximum consumption of clean energy in the park,with the goal of maximizing the ratio of output power and total load of equipment in the park,the objective function is established as follows∶

where is the output power of the distributed power supply at time t,PBC(t ) and H BC(t) are the output and heat power of the biomass CHP unit at time t,Hpump(t) is the output power of the wastewater source heat pump at time t,and Pload(t ) and H load(t) are the electrical and heat loads of the system at time t,respectively.

2.3 Operational constraints

The system operation constraints were divided into equipment operation,energy supply,and demand constraints [36].

1) Biomass CHP

The analysis in [37] concluded that biomass power generation is highly correlated with biogas pressure,biogas consumption,and power.Based on this analysis,a biomass CHP operation model is established as follows∶

where PB is the biomass CHP output power,FB and VB are the pressure and biogas consumption,respectively,α0 is the constant term coefficient,α1 and α2 are the linear term coefficients for the biogas generation pressure and biogas consumption volume,respectively,and α3 is the quadratic term coefficient.

The electricity and heat conversion efficiency of biomass CHP is∶

where is the biomass CHP heat power,λ is the biomass CHP heat conversion efficiency, is the biomass CHP electric power,and ξ is the biomass CHP electricity conversion efficiency.

In biomass treatment,the biogas supply is stable,and the power generation capacity of biomass CHP can be considered fixed for one day.From this,the electricity generation capacity constraint of the biomass CHP unit can be obtained as

The electricity generation output constraint of the biomass CHP unit is∶

where CB(t) denotes the remaining capacity of the CHP unit at time t,CBmax denotes the maximum capacity,P(t) denotes the output of the CHP unit at time t,and Pmax denotes the maximum power of the unit.

2) Energy storage operation constraints [38]

where SOCmin and SOCmax represent the upper and lower limits of the energy storage capacity,respectively,SOC (t)is the remaining power of the energy storage at time t,Pstorage(t ) is the operating power of the energy storage at time t,Pdis-max is the maximum discharge power,and Pch-max is the maximum charging power.

3) Electricity balance constraints

where Pgird(t ) is the power interacting with the grid at time t,PBC(t ) and PDGs(t) are the output power of the biomass CHP and distributed generation equipment at time t,respectively,PES−(t ) and PES+(t) are the storage charging and discharging power at time t,Pload(t ) is the system load at time t,Ppump(t)is the input power of the wastewater source heat pump at time t,and Peb(t) is the input power of the electric boiler at time t.

4) Heat balance constraint

where and are the heat power outputs of the biomass CHP and wastewater source heat pump at time t,respectively,is the heat power output of the electric boiler at time t,and H load(t ) is the heat load of the system at time t.

5) Heat-electricity ratio adjustment constraint [39]

where γBC(t) is the biomass CHP ratio at time t,and γmin(t)and γmax(t) are the lower,upper,and heat-electricity ratio adjustment limits at time t,respectively.

2.4 Introduction of the optimization algorithm

Because the township integrated energy system constructed in this study contains biomass,wind,solar,and other renewable energy sources,the system has high flexibility and uncertainty,involving multiple constraints,multiple variables,and nonlinearity.Its solution process is a typical non-deterministic polynomial (NP)-hard problem,and it is difficult for general optimal algorithms to obtain an optimal solution within an effective time [40-42].In addition,this study constructed the objective function considering the aspects of economy and clean energy consumption,and its operation optimization was different from that of a traditional power system.In actual operation,the economic and environmental benefits conflict with each other;therefore,it is necessary to find the Pareto optimal advantage and form an optimal scheduling scheme with comprehensive benefits.Intelligent evolutionary algorithms often offer more advantages than traditional algorithms [43,44].

PSO is a population iteration-based optimization technique that is essentially a multiagent parallel algorithm.It can easily optimize complex nonlinear problems [45] with the advantages of simplicity,generality,robustness,ease of implementation,high accuracy,and fast convergence.A flowchart of the PSO algorithm is shown in Fig.3.

Fig.3 Optimization flowchart of particle swarm optimization operation

Fig.4 Typical daily electricity and heat load curves

Fig.5 Prediction curves of wind turbine and photovoltaic output

The particle update formula in the PSO algorithm is expressed as∶

where c1 and c2 are learning factors that represent individual and collective experiences,respectively,also known as acceleration constants,r1 and r2 are random numbers in the range of 0-1,and D is the number of variables to be optimized in the objective function.

The process of PSO of the BIES was conducted as follows∶

Step 1∶ Basic data was input.The inputs included typical daily load data,energy prices,equipment pre-output data,equipment capacity,and operating parameters.

Step 2∶ The particle swarm was initialized and the parameters were set.The size and search dimensions of the particle swarm were determined,and its parameters were set,including the inertia coefficient,learning factor,maximum number of iterations,and maximum particle number velocity.

Step 3∶ The adaptation of individual particles in the initial population were calculated and the optimization strategy was used to calculate the target values of the BIES economy and energy-utilization rate.

Step 4∶ The particle update was performed.The adaptation of the current particle was compared with the best position.It was updated it if it was higher;otherwise,it remained the same.

Step 5∶ It was determined whether conditions were satisfied.The optimization was stopped and the result was output when the maximum number of iterations was reached or the accuracy requirement was satisfied;otherwise,return to Step 2.

Step 6∶ The optimization results were output.The system operation optimization results were analyzed,and the economic cost of operation and clean energy utilization rate were calculated.

3 Case study

3.1 Basic data

A town in northern China was used as the case study,and a simulation analysis was performed.This study disregarded the regional cold load because of its low demand,which can be satisfied by electricity.By comparing historical data and considering the demand for electric heating,we selected winter as the simulation object.According to the winter operation data,a typical daily load curve was formed for a large peak-to-valley difference,and an unstable load was selected to perform a simulation analysis of the operation optimization strategy of the proposed BIES,ensuring that the proposed strategy could satisfy the load demand at any time.The voltage of the distribution network in this region was 10 kV,and the optimal operation iteration time was 24 h.The BIES equipment constructed in this study included a wind turbine,PV unit,biomass CHP unit,wastewater source heat pump,energy storage system,and electric boiler.

This region relied on biomass CHP and wastewater source heat pumps for heating.Further,afterburning devices were incorporated into the biomass CHP units to increase the heat output by supplementing natural gas and providing additional heat supply through electric boilers in the event of excessive heat loads.The electrical load demand was provided by wind turbines,PV units,biomass CHP units,and power grids.

The region has a total daily electrical load demand of 111,083.21 kWh and a total heat load demand of 80,736.65 kWh.The wind turbine installed in the region had a capacity of 1200 kW,whereas the PV system had a capacity of 1000 kW.In addition,an energy-storage system with a capacity of 1000 kWh was used.The biomass treatment system for garbage and wastewater had an average daily capacity of 11.26 t and was equipped with a 2000 kW biomass CHP unit.The electricity price in the region followed the peak and valley time-division pricing schemes.

Tables 1 and 2 list the specific parameters.Table 1 lists the capacity of the energy supply equipment in the region,and Table 2 lists the energy price.

Table 1 Capacity configuration of energy supply equipment

Table 2 Natural gas and electricity prices

Typical daily load curves formed based on historical data for the wind turbine and the wind turbine and photovoltaic output curves are shown in Figs.4 and 5.

3.2 Scene setting and algorithm optimization

To assess the effectiveness of the proposed model,two operational scenarios were developed.

Scenario 1∶ The Biomass CHP unit did not include supplementary combustion equipment or normal operation.Operating costs were calculated at the lowest possible cost.The optimal operation scheme for each device was determined using the PSO algorithm.

Scenario 2∶ The Biomass CHP unit included supplementary burning equipment,and the system adjusted the power supply and heating scheme by adjusting the thermoelectric ratio of the unit.The operating costs were calculated based on the highest costs.

In this study,the PSO algorithm was used to determine the optimal operating plan for the BIES.The initial population and the number of iterations were set to 300 and 100,respectively.Because Scenario 1 did not require determining the heat-electricity ratio compared to Scenario 2,the solution was relatively simple;therefore,it reached convergence in the 21st generation.Because Scenario 2 required obtaining a suitable heat-electricity ratio using a PSO solution,the calculation process was longer.It converged to the optimal solution in the 34th generation.The adaptation curves for the two scenarios are shown in Fig.6.The Pareto-optimal curve obtained by solving is shown in Fig.7.

Fig.6 Convergence curves based on the algorithm

Fig.7 Particle swarm optimization results

Fig.8 Electricity system operation state

Fig.9 Operation state of the heat system

As evident from the Pareto curve,the economic benefits and environmental benefits were mutually exclusive;as the economic benefits increase,clean energy utilization will inevitably decline,and vice versa.To select the relatively optimal solution in the curve,the technique for order preference by similarity to ideal solution (TOPSIS) method was used to select the solution set,as shown in Fig.7.

3.3 Results analysis

3.3.1 Equipment output analysis

1) Electricity System

Figures 8(a) and (b) show the operating state of a typical daily electricity system in Scenarios 1 and 2,respectively.The power generation equipment of the electricity system included a wind turbine,a PV unit,a biomass CHP unit,and a grid.

Owing to the small installation capacity of power generation equipment and high regional load,the power supply was more dependent on the grid.In Scenarios 1 and 2,the load increased rapidly from 08∶00,and the power purchased from the grid increased.Whereas,the remaining instances accounted for a low power purchase from the grid.Compared with Scenario 1,as the heat output of the biomass CHP in Scenario 2 increased through the operation of afterburning devices,the electric boiler operated at a lower power and consumed less electricity;therefore,the power purchase from the grid decreased with the same output of other power generation equipment.For the energy storage,because the electricity must always be purchased from the grid in Scenario 1,the energy storage primarily operated in response to the peak-and-valley time division,charging at the valley hours of low price and discharging at the peak hours.In Scenario 2,the electricity supply was sufficient at 04∶00 and 05∶00;therefore,the energy storage was charged at this time and responded to the peak-tovalley time-division price at other times.Overall,after the heat-electricity ratio was adjusted,the purchased power was reduced,the operating cost of the system was reduced,the utilization rate of clean energy was improved,and the comprehensive benefits of the BIES were enhanced.

2) Heat system analysis

The operating states of a typical daily heat system under different scenarios are shown in Fig 9.The heat supply primarily relied on wastewater source heat pumps,biomass CHP systems,and electric boilers.

The operating states revealed that the heat demand of the system was primarily supplied by the wastewater source heat pump and the biomass CHP unit,supplemented by the electric boiler,and the efficiency of the heat pump was in the range of 3-3.5.In Scenario 2,the biomass CHP increased the heating output by adjusting the CHP ratio and reducing the operation of the electric boiler,thereby reducing the power consumption and operating cost of the system.

3.3.2 Analysis of optimization results

1) Economic costs

The operational results of the integrated-energy system can be obtained based on these operational states.The constructed model primarily included biomass treatment,equipment power generation,equipment heat supply,power purchase,and operation and maintenance costs.The operational optimization costs under different scenarios are shown in Fig.10.

Fig.10 Optimization costs of integrated-energy system operation

In Fig.10,as the electric boiler in Scenario 1 outputs more and consumes more electricity,the cost of the system operation optimization increases.The biomass CHP in Scenario 2 provided heat through afterburning and had a lower fuel cost than the electricity cost of the electric boiler.Therefore,its cost was lower than that of Scenario 1.

2) Clean energy utilization rate

In the integrated-energy system constructed in this study,the more biomass,wind,and solar energy was used,the higher the utilization rate of clean energy.Further,the electricity purchased from the grid reduced the utilization rate of clean energy.The results of the clean energy utilization rate under different scenarios are shown in Fig.11.

Fig.11 Clean energy utilization rate of the integratedenergy system

The clean energy utilization rate primarily considers the proportion of clean energy supply to the energy demand.In Scenario 1,the clean energy utilization rate was low because of the increased heat supply from the electric boilers.In Scenario 2,the clean energy utilization rate of the system was higher than that in Scenario 1 because the heat output of the system was increased by the afterburning devices,whereas the output of the electric boiler decreased.

3) Comparison of the results

The operational optimization results under different scenarios are presented in Table 3.In Scenario 1,the operating cost was 86,708.86 yuan and the clean energy utilization rate was 33.67%;in Scenario 2,the operating cost was 80,898.34 yuan and the clean energy utilization rate was 38.81%.The improvement ratios were 6.70% and 5.14%,respectively.Thus,in the constructed BIES,the adjusted heat-electricity ratio reduced the dependence on purchased energy,reduced the operating cost of the system,and improved the clean energy utilization rate,thus reducing CO2 emissions.

Table 3 Operation results

The system operating cost components are shown in Fig.12.

Fig.12 Cost components

Figure 12 shows that the highest proportion of the system’s operating costs were the costs of electricity purchased from the grid and fuel.In Scenario 1,the grid power purchase cost was higher owing to the additional consumption of the electric boiler,accounting for 78%,whereas the fuel cost accounted for 11%.In Scenario 2,afterburning was applied to the biomass CHP,which increased the fuel cost of the system by 15%;however,the proportion of the grid power purchase cost was reduced to 73%.The operating costs of wind turbines,PV systems,and energy storage were stable,accounting for 11% in Scenario 1 and 12% in Scenario 2.The cost composition further demonstrated the feasibility of the proposed heat-electricity ratio adjustment plan.

3.4 Comparison of different scenarios

To compare the comprehensive benefits of the BIES and highlight the role of the biomass processing system in ensuring the system benefits,we constructed different application scenarios for operational optimization using the PSO algorithm.The system structures of Scenarios 1 and 2 were kept unchanged,and three other scenarios were added for comparison.Scenario 3 did not use a wastewater source heat pump,and was replaced by an electric boiler for heating.Scenario 4 used a wastewater source heat pump and a CHP unit to form an integrated energy system.Scenario 5 selected the electric boiler and CHP unit for operation.The economic costs and energy utilization rates of the system under different scenarios are listed in Table 4.

Table 4 Comparison of the results of different application plans

As evident,compared with other scenarios,the use of a sewage source heat pump and biomass cogeneration can effectively reduce economic costs and improve the utilization rate of clean energy.Compared with Scenario 5,Scenario 1 used a sewage source heat pump and biomass energy;the system operating cost was reduced by 9.97%and the clean energy utilization rate was increased by 8.27%.After increasing the utilization of low-grade heat energy by the sewage source heat pump,the system operating cost was reduced by 6.88%,and the clean energy utilization rate was increased by 5.04% compared with the biomass energy in Scenarios 4 and 1.Scenario 3 used biomass cogeneration without recycling the heat energy in sewage.Compared to Scenario 3,Scenario 1 reduced the cost by 5.77% and increased the utilization rate of clean energy by 3.82%.within terms of sewage heat energy,the full use of biomass energy can further improve the economic and environmental benefits of the system.After adding secondary combustion equipment to adjust the thermoelectric ratio in Scenario 2,the operation optimization results were further improved.

In conclusion,the proposed biomass energy system significantly reduced expenses and enhanced the utilization rate of clean energy.The use of afterburning devices to regulate the heat-electricity ratio further amplified these advantages,leading to a 6.70% decrease in costs and a 5.14% increase in the clean energy utilization rate.The synchronized operation of the biomass CHP and wastewater source heat pump systems within the BIES guaranteed the adaptability of the system and effective control of its operational optimization costs.

4 Conclusion

Against the background of “carbon peaking and carbon neutrality”,clean energy will gradually become the primary energy source to support the social and economic development of China.Biomass energy,as a renewable green energy source with low pollution and wide distribution,has significant application potential in integrated energy systems.Biomass energy has broad development prospects as a renewable green energy source with low pollution and wide distribution.Considering the economic and environmental benefits,this study established the objective functions of the lowest economic cost and highest clean energy utilization rate,and proposed constraints for the equipment operation state.Different operation strategies for the system were proposed based on whether afterburning devices were incorporated.Thereafter,the PSO algorithm was selected for the simulation analysis to obtain the operation optimization plan of the biomass integrated-energy system.

The simulation results demonstrated that the optimization of biomass energy operations effectively reduced energy costs and improved clean energy utilization efficiency compared to other forms of energy utilization.Adjustment of the heat-electricity ratio further reduced economic costs by 6.70% and increased clean energy utilization rates by 5.14%.These findings highlight the potential of constructing BIESs to support carbon peaking and carbon neutrality targets.

Currently,the use of integrated energy systems is expanding significantly,and the development of integrated energy systems to achieve regional multi-energy complementarity is prominent.In this context,future research should explore the application of biomass energy and integrate the planning,construction,and operation optimization of biomass energy with integrated-energy systems.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (U2066211).

Declaration of Competing Interest

We declare that we have no conflict of interest.