Combined Heat And Power Plant Research Articles

Study of the differences in collection scope of raw materials of biomass CHP plants caused by regional factors

Combined heat and power (CHP) plants fueled by biomass can collect and utilize waste straw resources in a productive way. This paper considers the impact of regional factors on biomass energy potential and the energy needs of the population, so as to study the differences in construction of biomass CHP plants and the collection scope of raw materials, and proposes evaluating suitability for biomass energy development based on scope of resource collection. Taking five counties in China as its study areas, this paper assesses biomass energy potential. A topology system of biomass CHP plants has been reasonably established in different counties through ArcGIS, the required installed capacity has been calculated according to the number of persons served by such plants. Finally, the collection length and corresponding value range of raw materials of CHP plants along roads has been obtained based on biomass energy potential and energy demand. The result shows that the differences in area, straw yield and biomass fuelization rate depending on regions have a great impact on biomass energy potential, while the residue-to-product ratio of straw and biomass calorific value have less of an impact. When the biomass energy per capita of a region reaches 9.75GJ/person, it is suitable for biomass energy development. The installed capacity in the biomass CHP plant system of each study area is mostly within the scope of 3–59 MW, and the collection length of corresponding biomass resources of such plants along roads is mostly within the scope of 5.09–25.23 km.

A flexible and deep peak shaving scheme for combined heat and power plant under full operating conditions

Improving the flexible and deep peak shaving capacity of combined heat and power (CHP) plant under full operating conditions to facilitate renewable energy consumption is the main choice of novel power system. Accordingly, a dry/wet state automatic conversion control scheme fuses the precise mechanism structure, reinforcement learning and multi-objective model predictive control (MPC) algorithms is designed to promote the deep peak shaving ability of CHP plant in this paper. Firstly, the detailed mechanism models of once-through boiler at dry and wet state are presented by analyzing the dynamic characteristics of steam-water flow. Secondly, the large-scale unknown parameters in mechanism models are identified via the Takagi-Sugeno fuzzy modeling, reinforcement learning algorithms and actual dry/wet state conversion data. Thanks to the proposed hybrid modeling strategy, the high-precision boiler-steam unit models at dry and wet state are rapid obtained. Then, to meet different peak shaving demands, the multi-objective MPC algorithm is respectively constructed under dry and wet state with the comprehensive consideration of operational constraints, load tracking error, algorithm stability, power generation cost, CO2 and NOx emission costs. Aiming at maximizing the peak shaving efficiency and flexible operation ability of plant, a dry/wet automatic control scheme combined the designed multi-objective MPC algorithms and identified dry/wet state models is proposed. Finally, the load variation rate of 5 % and 2 % RCM/min is respectively achieved in the dry/wet state conversion tests on a 350 MW CHP plant based on the proposed control scheme. Thus, the rapid and thorough dry/wet state conversion performance of once-through boiler has successfully improved the operational flexibility of CHP plant under full operating conditions.

Cost-benefit analysis and comparison of grid-stabilizing energy flexibility options and their applications in relation to the German energy system

With an increasing share of renewable energies, flexibility is becoming an important factor to ensure the German electricity grid stability. Therefore, decision-makers have to deal with the implementation of the necessary transformations and adaptions. To provide an indication, a cost–benefit analysis and comparison of grid-stabilizing energy flexibility options and their applications for a current and an outlook scenario is conducted. In the results, due to its low costs, the flexibilization of existing CHP (combined heat and power generation) plants stands out as a particularly attractive option. Similarly, the flexibilization of existing wind turbines and photovoltaic systems, as well as the flexibilization of small heat pumps, appear to be highly attractive in the outlook (integration is enabled by pooling). Furthermore, in the future, large-scale batteries and PEM (Polymer electrolyte membrane) electrolysis as well as controlled charging (pooling) will become increasingly beneficial for the energy system. The overall worst performing flexibility option is power-to-methane, which requires additional effort from the system in both scenarios. In general, the research showed that even for similar flexibility options, the resulting systemic benefit highly depends on the specific flexibility application. Nonetheless, the results give an indication towards important sources of flexibility and further research priorities.

HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation

Gas usage estimation plays a critical role in various aspects of the power generation and delivery business, including budgeting, resource planning, and environmental preservation. Federated Learning (FL) has demonstrated its potential in enhancing the accuracy and reliability of gas usage estimation by enabling distributedly owned data to be leveraged, while ensuring privacy and confidentiality. However, to effectively motivate stakeholders to contribute their high-quality local data and computational resources for this purpose, incentive mechanism design is key. In this paper, we report our experience designing and deploying the Hierarchical FL Incentive mechanism for Gas usage estimation (HiFi-Gas) system. It is designed to cater to the unique structure of gas companies and their affiliated heating stations. HiFi-Gas provides effective incentivization in a hierarchical federated learning framework that consists of a horizontal federated learning (HFL) component for effective collaboration among gas companies and multiple vertical federated learning (VFL) components for the gas company and its affiliated heating stations. To motivate active participation and ensure fairness among gas companies and heating stations, we incorporate a multi-dimensional contribution-aware reward distribution function that considers both data quality and model contributions. Since its deployment in the ENN Group in December 2022, HiFi-Gas has successfully provided incentives for gas companies and heating stations to actively participate in FL training, resulting in more than 12% higher average gas usage estimation accuracy and substantial gas procurement cost savings. This implementation marks the first successful deployment of a hierarchical FL incentive approach in the energy industry.

Performance evaluation and operation optimization of a combined heat and power plant integrated with molten salt heat storage system

Combined heat and power is an effective way to enhance the energy efficiency of thermal power plants. The solar and wind power are intermittent, and dispatchable power sources are critical to balance the supply and demand sides. To facilitate the high penetration of renewable energy, combined heat and power (CHP) plants should provide more and more peak shaving services for the power grid. The thermal energy can be stored in and released from the molten salt heat storage system (MSHSS). The integration of MSHSS can enlarge the adjustable range of the power load of CHP plants, so it is a potential way to enhance the operational flexibility of CHP plants supplying industrial steam and power. In this study, the MSHSS integrated within CHP plants are proposed, which store a part of sensible heat energy carried by extraction steam before supplying to industrial users. The heat storage capacity dominates the flexibility enhancement, and indicators to evaluate energy performances of the proposed system are defined. Moreover, models for the operation optimization of CHP plants integrated with the MSHSS are developed, and a case study of a 350 MW CHP unit is conducted. When the reheat steam is extracted to supply to industrial steam, the maximum and minimum adjustable power loads can be increased and decreased by 17.13 and 12.01 MW, respectively with the integration of the MSHSS. Moreover, during the peak, flat, valley time of reference day, the heat supply coal consumption rates are 73.94, 74.19 and 81.03 kg t−1,respectively. With the operation scheduling of CHP plant integrated with MSHSS, the MSHSS with reheat steam as heat source show the lowest coal consumption rate as 1553.79 ton/day.

Productivity of two Brassica oilseed crops in a Mediterranean environment and assessment of the qualitative characteristics of raw materials for bioenergy purposes

Rapeseed (Brassica napus var. oleifera D.C.) and Ethiopian mustard (Brassica carinata A. Braun) are promising industrial crops for cultivation in the Southern Mediterranean area due to profitable yields under semi-arid conditions. The exploitation of raw materials produced by these crops is very convenient for farmers to produce bioenergy directly on-farm and permits them to create a short agri-energy supply chain. The purpose of this study was to determine their yield performance under rainfed conditions and make an economic assessment of a combined heat and power plant (CHP) system operating on pure vegetable oil (PVO). Tests were conducted in Sicily (Italy) from 2012 to 2014. Seed and crop residue yields were detected. The analysis of seed, defatted seed meal and crop residue, and the chemical-physical aspects of PVO were carried out according to conventional protocols. A pilot CHP system was used for cogenerating electricity and heat. In general, rapeseed had the highest seed (2.27 t ha−1) and oil (1.11 t ha−1) yields. The average oil content ranged from 44.88 % (Ethiopian mustard) to 45.73 % dry matter (rapeseed). Ethiopian mustard performed better than rapeseed in terms of aboveground biomass yield (5.49 t ha−1), in both years. The two crops showed different fatty acid profiles of the oil mainly due to diverse content of erucic and oleic acids. The CHP system had an average consumption of 14.41 kg PVO h−1. These results confirm that the productivity of the species can be appreciable in the Southern Mediterranean area and indicate the use of raw materials of these crops as crucial to the development a sustainable short agri-energy supply chain.

A robust optimization approach for resiliency improvement in power distribution system

AbstractThe occurrence of natural disasters has led to an alarming increase in power interruptions, with severe impacts. Compounding this problem is the uncertain nature of data, which presents significant challenges in enhancing the resiliency of power distribution systems following such events. To tackle these issues, this paper introduces a robust optimization approach for improving the resiliency of power distribution systems. The approach encompasses crew teams for switching actions as part of the restoration process, along with demand response programs and mobile generators (MGs). By simultaneously leveraging these elements and considering the uncertainty associated with electrical load and electrical price, the resiliency of the system is enhanced. The objective function is tri‐level, comprising minimum, maximum, and minimum functions. At the first level, the approach minimizes the cost of commitment of combined heat and power plants (CHPs) by taking into account the location of MGs and the reconfiguration structure in power distribution systems. The second level aims to identify the worst‐case scenario for the uncertainty variables. Finally, the third level focuses on minimizing the total operation cost under the worst‐case scenario using demand response programs. The proposed algorithm is implemented on an IEEE 33‐bus test distribution system, with four different cases investigated.

Current state and approaches to automation of central heating stations at industrial enterprises

The paper examines the problems of forming technical requirements for the automation of central heating points of fuel and energy complex enterprises. The main technological schemes that are subject to automation are considered, the basic requirements for the installation of technical automation equipment are shown, the basic requirements are identified based on modern concepts for constructing automated control systems, and the main adjustable and controlled parameters of the central heating station for the main application in State Unitary Enterprise “Fuel and Energy Complex” are clarified.

Nearly-zero carbon optimal operation model of hybrid renewable power stations comprising multiple energy storage systems using the improved CSO algorithm

In light of the abundant renewable energy resources in Northwestern China, this study introduces a novel hybrid power plant structure known as the (Renewable energy-concentrating solar power-combined heat and power) RCC system. The RCC system integrates various energy sources, including the photovoltaic, the wind power plant, the concentrating solar power (CSP) plant, and the combined heat and power (CHP) plant. It also incorporates power-to-gas (P2G) technology to convert wind and solar power surpluses into methane. Moreover, carbon capture and storage (CCS) technology is applied to capture carbon dioxide emissions from the CHP plants, which serves as a raw material for the P2G process. To address the energy trilemma, we develop a nearly-zero carbon emission optimization model for the RCC system, considering different renewable energy source (RES) endowments. The fuzzy membership function method is employed to identify the optimum satisfaction target across multiple attributes. An enhanced CSO algorithm is introduced and validated using a simulation study on a CSP plant in Dunhuang. The results show that compared with traditional power stations, the proposed RCC system can reduce the investment cost by 34.45 %, increase the operating income by 17.7 %, and reduce carbon emission by 3.2 %. At the same time, the methane stock in the system increased by 53.7 % compared with the traditional power station containing P2G equipment, which helps the hybrid power station to participate in the methane market and obtain more profits. In addition, the direct energy supply ratio of RES decreased by 83.16 %, reducing the risk of the system caused by the uncertainty of RES. To sum up, the proposed RCC system has a good development prospect.

Techno-Economic Analysis of a Highly Renewable and Electrified District Heating Network Operating in the Balancing Markets

In pursuit of Finland’s carbon neutrality objective by 2035, integrating renewable energy sources into the power grid is essential. To address the stochastic nature of these resources, additional sources of flexibility are required to maintain grid stability. Meanwhile, district heating network (DHN) operators in Finland are decommissioning fossil fuel-based combined heat and power plants (CHPs) and electrifying heating systems with heat pumps (HPs) and electric boilers. A techno-economic assessment and the optimized operation of DHN-connected HPs and electric boilers in providing ancillary balancing services were explored in this study. The primary goal was to maximize the potential revenue for DHN operators through participation in the day-ahead electricity market and frequency containment reserve (FCR) balancing markets. Three interconnected DHNs in the Helsinki metropolitan area were optimized based on 2019 data and each operator’s decarbonization strategies for 2025. HPs are expected to achieve the highest profit margins in the FCR-D up-regulation market, while electric boilers could generate substantial profits from the FCR-D down-regulation market. In contrast to other balancing markets studied, the FCR-N market exhibited limited profit potential. Sensitivity analysis indicated that spot electricity prices and CO2 emission allowance prices significantly influence the profitability derived from balancing markets.

Battery–integrated combined cooling, heating and power plant (CCHP) through NH3 – H2O absorption system in a hospital facility

Combined heat and power (CHP) systems have been employed in various applications for years. They are gaining increasing attention in the residential and small industrial sectors for their primary energy saving potential and CO 2 emissions reduction. Additionally, in specific applications such as the hospital sector, CHP plants can play a critical role in replacing fossil-fueled electric generators to supply electricity during local grid outages, thus enhancing hospital facilities’ energy efficiency, while also securing reliability and efficiency of operation. Hospitals have unique energy demand profiles, with high and constant demand (particularly heating and cooling demands), making them an ideal use-case for trigeneration systems. Combined, cooling, heating and power plants (CCHP) are capable to provide not only heating and electricity, but also cooling through the efficient exploitation of a single energy source, reducing Hospitals’ reliance on the local grid. In this context, this work aims at evaluating the potential of a battery-integrated CCHP plant through an innovative ammonia-water absorption system, whose energy analysis is based on data acquired from an Italian Hospital facility. The potential of integrating an experimental combined cooling and power production ammonia-water absorption system has been investigated starting from an optimized battery energy storage system (BESS) CHP plant configuration, studied for the same facility. A proper control strategy has been developed to maximize the cooling production when required from the end-user through CHP plant’s exhaust gases waste heat recovery. The energy analysis demonstrated the advantages of the CCHP-BESS plant over the optimal CHP-BESS configuration, with an 11.22% increase in primary energy saving, a 9.85% reduction in CO 2 emissions, and a 5.03% decrease in electric peak power demand.

Quantification of cogeneration vulnerability towards the climate change: framework development

Cogeneration is a vigorous measure to both decrease greenhouse gases emissions and improve an overall efficiency of the primary energy use. The climate change poses serious risks on operation of combined heat and power (CHP) plants due to the shifts in heating load patterns. This work investigates linking of the heating demand to the climate conditions and introduces a framework which can be used to develop an adaptation strategy for district heating systems. We demonstrate viability of the proposed approach by a case study assessing sensitivity of the CHP efficiency to the climate warming across China considering a modern combined-cycle gas-fired power plant with steam extraction. Outputs of the case study have demonstrated that CHP plants are generally more vulnerable towards the climate change in world regions with harsher climate conditions. An effective measure to increase resilience of the cogeneration sector is to account for anticipated climate change effects by applying adjustments to the reference climate inputs used when designing CHP plants.

Characterization and modification of biochar from a combined heat and power (CHP) plant for amending sandy soils collected from wild blueberry fields

Wild (or lowbush) blueberries (Vaccinium angustifolium Ait.) undergo severe drought impacts due to climate warming because they grow in sandy soils with poor water retention. The feasibility was studied for using biochar in a forest biomass-fueled combined heat and power (CHP) plant to amend the sandy soils. The chemico-physical properties (e.g., bulk density, moisture content, porosity, pH) of the biochar were measured. An acid treatment method (1% to 3% acidic or citric acid solution) was developed to decrease the biochar pH from 11.4 to neutral or lower, aiming to aid in weed control in wild blueberry fields. The water holding capacity (WHC) of sandy soils (S) mixed with biochar (B) (Type I) and sandy soils mixed with both biochar and fertilizer (Type II) at four ratios of 100S:0B (control), 50S:50B, 30S:70B, and 10S:90B were measured. The biochar generated from the CHP plant had comparable physical properties (such as bulk density, porosity, pH, and surface area) with woody biochar made from pyrolysis. The acid treatment method significantly lowered the pH to a range of 5.0 to 6.5. The 50:50 mixing ratio for both Type I and Type II increased the water holding capacity by about 20% compared with control groups.

Selecting the approach for dynamic modelling of CO2 capture in biomass/waste fired CHP plants

Integrating CO2 capture with biomass/waste fired combined heat and power (CHP) plants is a promising method to achieve negative emissions. However, the use of versatile biomass/waste and the dynamic operation of CHP plants result in bigger fluctuations in the properties of flue gas (FG), e.g. CO2 concentration (CO2vol%) and flowrates, and the heat that can be used for CO2 capture, when comparing with coal fired power plants. To address such a challenge, dynamic modelling is essential to accurately estimate the amount of captured CO2 and optimize the operation of CO2 capture. This paper compares three dynamic approaches commonly used in literature, namely using the ideal static model (IST) and using dynamic models without control (Dw/oC) and with control (DwC), for MEA based chemical absorption CO2 capture. The performance of approaches is assessed under the variations of key factors, including the flowrate and CO2vol% of FG, and the available heat for CO2 capture. Simulation results show clear differences. For example, when the CO2vol% drops from 15.7 % to 9.7 % (about 38 %) within 4 hours, DwC gives the highest amount of captured CO2, which is 7.3 % and 22.3 % higher than IST and Dw/oC, respectively. It is also found that the time step size has a clear impact on the CO2 capture amount, especially for DwC. Based on the results, suggestions are also provided regarding the selection of dynamic modelling approaches for different purposes of simulations.

Assessing the CO2 capture potential for waste-fired CHP plants

The integration of CO2 capture with biomass-fired power plants has attracted much attention due to its ability to achieve negative emissions. Waste-fired combined heat and power (CHP) plants with CO2 capture, on the other hand, has received little attention, and their potential remains unclear. This study aims to identify the possible range of the amount of captured CO2 and investigate the impact of CO2 capture on the performance of waste-fired CHP plants. Since heat is the primary product of CHP plants, it is important to maintain heat production unchanged when CO2 capture is integrated. Based on this prerequisite, two operating strategies (OS) were investigated, which correspond to the upper and lower boundaries of CO2 capture: OS1 was to maximize the amount of captured CO2 while keeping the heat supplied to the district heating (DH) network unchanged; and OS2 was to maximize CO2 capture while keeping both supplied heat and generated electricity unchanged. To obtain more accurate results regarding the CO2 capture, a dynamic model developed in Aspen Hysys™ was utilized to simulate monoethanolamine (MEA) based chemical absorption for CO2 capture. By using real dynamic data from a waste-fired CHP plant, dynamic simulation results showed that the highest amount of captured CO2, which was achieved in OS1, was 401 kton/year, corresponding to a CO2 capture ratio of 82%; while the lowest amount of captured CO2, which was achieved in OS2, was 99 kton/year, corresponding to a CO2 capture ratio of 20%. For OS1, the electricity generation was substantially decreased by 61%. When determining the negative emission, the emission resulted from the share of fossil fuel in the waste needs to be excluded. For the studied CHP plant, the fossil share was around 45%. As a result, only OS1 can achieve the negative emission, which was 181 kton/year; while OS2 still led to positive emissions. Compared to the plant without CO2 capture, the carbon intensity of heat was reduced from 0.405 ton/MWh to 0.091 ton/MWh in OS1 and 0.351 ton/MWh in OS2, while the carbon intensity of electricity was reduced from 0.409 ton/MWh to 0.072 ton/MWh in OS1 and 0.343 ton/MWh in OS2.

Assessing the Impact of Combined Heat and Power Plants (CHPPs) in Central Asia: A Case Study in Almaty for PM2.5 Simulations Using WRF-AERMOD and Ground Level Verification

According to the World Health Organization, Kazakhstan is one of the most polluted countries in the world. PM2.5, a major air pollutant, is six times higher than the recommended value of 5 mg/m3. The government has implemented measures to reduce air pollution, such as introducing green energy-powered buses for public transportation, but the results have not been sufficient. Therefore, it is necessary to investigate the sources of PM2.5. This study involved simulating the Combined Heat and Power Plants (CHPPs) emissions in Almaty using AERMOD and WRF for two weeks in January 2021. Two scenarios were performed: controlled and uncontrolled. The results showed that if the control mechanism of the CHPP functions at maximum efficiency, the impact of the CHPP emissions on the total emission concentration will be negligible, which is about 6% on average. However, for uncontrolled CHPPs, the emissions will contribute from 30% to 39% on average to the total PM2.5 concentration when compared with data from US Embassy monitoring stations and public air quality monitoring network, which use Pms5003 PM2.5 sensors.

Optimal Scheduling Framework of Integrated Energy System Based on Carbon Emission in Electricity Spot Market

Micro coal-fired units (MCFU) and combined heat and power plants (CHP) in integrated energy system (IES) will emit a large amount of carbon dioxide when providing loads to customers, which will lead to higher operating costs of IES. To solve this challenge, an optimal dispatch model of power-to-gas (P2G) and methane reactor (MR) considering the reward and punishment costs based on carbon emission trading mechanism is proposed, to reduce carbon emissions of IES and enhance the accommodation of renewable energy (RE). Subsequently, considering the uncertainty of RE, a combination optimization method for MCFU and CHP units is developed based on the Lagrange multiplier method. Finally, considering the mechanism of electricity spot market (ESM), a transaction strategy for IES participating in ESM is proposed to further enhance the accommodation of RE. The effectiveness of the proposed framework is demonstrated through simulations.

Optimal Configuration of Power-to-Heat Equipment Considering Peak-Shaving Ancillary Service Market

The serious problem of wind power curtailment in northern China has created a pressing need to enhance the peak-shaving ability of the power system. As the main source of power supply in northern China, combined heat and power (CHP) units have significant potential for peak-shaving. Currently, the Chinese government encourages CHP plants to increase their peak-shaving capacity by installing power-to-heat (P2H) equipment. In addition, the government has implemented auxiliary service market policies to encourage CHP plants to provide peak-shaving services. In order to maximize economic benefits for CHP plants, this paper proposes an optimal configuration method of P2H equipment with the static payback time (SPT) as the objective function. Cost and income models of installing the P2H equipment are constructed by taking into account the auxiliary service market policies. The peak-shaving income model of the CHP plant is derived emphatically as a key part of the proposed method. Finally, the district heating region in Jilin province is used as a case study example. The results show that adding the P2H equipment is significantly effective in improving the peak-shaving ability of CHP units, and investing in heat pumps is more cost-effective than electric boilers. The proposed method can be applied to other northern regions relying on CHP units for central heating, providing a valuable solution to the problem of wind power curtailment in these regions.

Optimal control of combined heat and power station operation

Combined heat and power stations have become one of the most utilized units of district heating systems. These stations usually contain several boilers for burning fossil fuels and renewable resources used for heating up steam, which can be used either for residential and commercial heating or electricity generation. To ensure efficiency, a boiler should either run continuously (for at least a given period) on a power output higher than a given threshold or switch off. The optimal control of the plant operations should combine an efficient setup for the turbine and boilers in operation, reflecting the demand for steam and the price of electricity, and a schedule that describes which boilers should be in operation at a given time. This paper proposes a method for optimal control of combined heat and power station operation for a given time horizon. The method is based on a two-level approach. The lower-level models correspond to finding the optimal setup of the combined heat and power station parameters for an hourly demand for different kinds of steam. The upper-level model corresponds to the optimal schedule of the operations of the individual boilers, which is planned for the entire time horizon. The lower-level model is modeled as a mixed-integer linear programming problem and is solved using parametric programming. A dynamic programming algorithm solves the upper-level model with a rolling horizon. The validity of the proposed method and its computational complexity for different granularity of the time horizon, different ranges of the parameters, varying demand for various kinds of steam, and varying electricity prices are investigated in a case study. The presented approach can be readily applied to other control problems with a similar structure.

Carbon capture from combined heat and power plants – Impact on the supply and cost of electricity and district heating in cities

The capture and storage of biogenic CO2 emissions from large point sources, such as biomass-combusting combined heat and power (CHP) plants, can contribute to climate change mitigation and provide carbon-negative electricity while supplying district heating in urban areas. This work investigates the impact of retrofitting CO2 capture processes to CHP plants in a city energy system context. An energy system optimization model is applied to a case study of the city Västerås, Sweden, with scenarios involving two existing CHP plants in the city, retrofitted with either a heat-driven (MEA) or an electricity-driven (HPC) carbon capture process. The results show that the CHP plants might be retrofitted with either option without significantly impacting the district heating system operation or the marginal costs of electricity and district heating in the city. The MEA process mainly causes a reduction in district heating output (up to 30% decrease on an annual basis), which can be offset by heat recovery from the capture unit. The electrified HPC process does not impact the CHP plant steam cycle but implies increased import of electricity to the city (up to 44% increase annually) compared to a reference scenario.