Combined Cooling, Heating And Power Research Articles

Optimal Operation of Energy Hubs With Large-Scale Distributed Energy Resources for Distribution Network Congestion Management

Congestion problems might occur in distribution networks as the penetration of distributed energy resources (DERs) progresses. This study focuses on the complementarity of multiple energy resources in energy hubs (EHs) to solve possible distribution network congestions. First, we consider an EH in which combined cooling, heating and power (CCHP) units and heat pumps are integrated with renewable energy resources. The EH models the intrinsic coupling relationship among various energy carriers, forming a flexible operation where electricity, natural gas, cooling and heat can complement each other. Next, an optimal operation strategy of multiple energy hubs enables gas-to-electricity to provide local energy supply for EH during electricity peak periods, and consume renewable energy generation by the complementarity of electricity, heat and cooling. In addition, the uncertainty of renewable energy resources is taken into account via scenario-based stochastic programming. Numerical results show that the proposed operation strategy explores EHs’ operation flexibility to mitigate distribution network congestion when we consider high renewable energy penetration in power systems.

A Simulation Method Based on the Candidate Operation Strategies for the Combined Cooling, Heat, and Power (CCHP) System

With the change of the distributed Combined cooling, heat, and power (CCHP) system operation environment, such as the operation under the power market, CCHP needs to adopt the operation strategy based on cost minimization instead of the traditional following thermal load strategy (FTL). However, the current CCHP system simulation adopts FTL or FEL or a combination of the two; whether these strategies have the least cost has not been confirmed. In this paper, all kinds of operation strategies of the CCHP with two cooling methods are investigated according to the KKT conditions (Karush–Kuhn–Tucker conditions) of the optimal operation model. Among all the feasible operation strategies, most of them do not meet the KKT condition, and only 43 strategies may be the optimal ones that constitute candidate set. The conditions of the optimal operation strategy in the candidate set are obtained, and it is easy to calculate, so the optimal operation strategy can be easily selected from the candidate set. Some previously unnoticed strategies, for example, TEWM, PE-FPG, FTL-R, and FEL-L, may become optimal. A rule-based CCHP simulation method is established that selects the strategy with minimal cost as the optimal strategy from the candidate strategy set. The presented method has small time consumption and can simulate the optimal operation. The case study verified the characteristics of the proposed simulation method.

Stochastic multi-scenario optimization for a hybrid combined cooling, heating and power system considering multi-criteria

Combined cooling, heating and power (CCHP) system integrated with multiple renewable energies can improve the environmental performance while increasing the dependency on the national grid due to the multiple uncertainties. This paper proposes a multi-objective stochastic multi-scenario optimization method for the optimal capacity of a CCHP system integrated wind, solar and geothermal energy considering its energy supply independence, environmental impact, economic performance and energy efficiency. The spatiotemporal multiple uncertainties in wind velocity, solar irradiation and multiple loads are characterized by the stochastic multi-scenarios generated by the stochastic hierarchy scenario-generation method. The hybrid system's flexibility is evaluated by both the grid integration level and net interaction level. The environmental performance is assessed by both the carbon emission reduction rate and renewable energy penetration. The economic and energy performances are characterized by the annual cost-saving rate and primary energy-saving rate, respectively. The multi-objective stochastic optimal design method is formulated and solved using non-dominated sorting genetic algorithm II. The case study results show that there are slight deviations of objectives between the stochastic multi-scenario and traditional optimizations, while the former can save 58.69% computation time. Moreover, the installed capacity increase of the electrical energy storage to 870kWh can reduce the system's dependency on the national grid and the net interaction level to 3.74% while deteriorating the economic performance and dropping the annual cost-saving rate to −217.37%. Inversely, the installed capacity increase of renewable energy generators can enhance economic and environmental performances while worsening the net interaction level.

Classification and Analysis of Optimization Techniques for Integrated Energy Systems Utilizing Renewable Energy Sources: A Review for CHP and CCHP Systems

Energy generation and its utilization is bound to increase in the following years resulting in accelerating depletion of fossil fuels, and consequently, undeniable damages to our environment. Over the past decade, despite significant efforts in renewable energy realization and developments for electricity generation, carbon dioxide emissions have been increasing rapidly. This is due to the fact that there is a need to go beyond the power sector and target energy generation in an integrated manner. In this regard, energy systems integration is a concept that looks into how different energy systems, or forms, can connect together in order to provide value for consumers and producers. Cogeneration and trigeneration are the two most well established technologies that are capable of producing two or three different forms of energy simultaneously within a single system. Integrated energy systems make for a very strong proposition since it results in energy saving, fuel diversification, and supply of cleaner energy. Optimization of such systems can be carried out using several techniques with regards to different objective functions. In this study, a variety of optimization methods that provides the possibility of performance improvements, with or without presence of constraints, are demonstrated, pinpointing the characteristics of each method along with detailed statistical reports. In this context, optimization techniques are classified into two primary groups including unconstrained optimization and constrained optimization techniques. Further, the potential applications of evolutionary computing in optimization of Integrated Energy Systems (IESs), particularly Combined Heat and Power (CHP) and Combined Cooling, Heating, and Power (CCHP), utilizing renewable energy sources are grasped and reviewed thoroughly. It was illustrated that the employment of classical optimization methods is fading out, replacing with evolutionary computing techniques. Amongst modern heuristic algorithms, each method has contributed more to a certain application; while the Genetic Algorithm (GA) was favored for thermoeconomic optimization, Particle Swarm Optimization (PSO) was mostly applied for economic improvements. Given the mathematical nature and constraint satisfaction property of Mixed-Integer Linear Programming (MILP), this method is gaining prominence for scheduling applications in energy systems.

An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties

The combined cooling, heating and power (CCHP) microgrid has the advantages of promoting cleaner production and improving energy utilization efficiency. With the development of renewable energy sources (RES), it is more and more significant to study the optimal operation of CCHP based on the uncertainties of RES outputs. This paper proposes a CCHP-P2G microgrid system, which combined the power-to-gas (P2G) device with traditional CCHP microgrid. And a Data-driven Set based robust optimization (DSRO) model considering the uncertainties of wind power and multiple demand response programs (DRPs) have been presented, which consists of two stages: Day-ahead dispatching stage and Real-time adjusting stage. Simulations are delivered to show the following outcomes: (1) the P2G device can improve the electricity-gas coupling in the CCHP-P2G system, enhancing the system’s stability and economy of the system operation; (2) the multiple DRPs in the DSRO model can play the role of peak shaving and valley filling of electrical load, heating load and cooling load, so as to further reduce the operating cost of the system; (3) the DSRO model can resist the interferences of uncertain wind power outputs and keep both the conservativeness and computational complexity in relatively low levels.

Robust multi-objective optimization with life cycle assessment of hybrid solar combined cooling, heating and power system

Hybridization of solar energy technologies results in varieties of inherent characteristics of natural gas combined cooling, heating and power (CCHP) system. This work aims to propose a life cycle assessment-based robust multi-objective optimization model of hybrid solar-assisted CCHP system, which is driven by the complementarities of photovoltaic/thermal collectors and gas turbine. The environmental impact potentials including global warming, respiratory effect and acidification of hybrid system are assessed according to life cycle energy and emission inventory analysis. The optimal plan of hybrid system, using nondominated sorting genetic algorithm II, is implemented to minimize these environmental impacts, in which the robustness is incorporated as part of the search process of genetic algorithm. The sensitivity analysis of candidate Pareto solutions by transferring to linear programming sub-problem drives to the selection of new solutions with robustness requirements. A case study in a hotel building was presented to demonstrate the proposed optimization method. The key uncertain factors on optimization and system performances are discussed. The comparisons of partially and fully covered photovoltaic/thermal collectors indicate that the partially covered collectors lower the impacts of respiratory effect and acidification by 28.4% and 6.7%, respectively, by optimizing system configurations and energy compositions, and the optimum partially covered collectors with CCHP system is more cost-effective and feasible.

Performance evaluation and exergy analysis of a novel combined cooling, heating and power (CCHP) system based on liquid air energy storage

Liquid air energy storage (LAES) is a promising large-scale energy storage technology in improving renewable energy systems and grid load shifting. In baseline LAES (B-LAES), the compression heat harvested in the charging process is stored and utilized in the discharging process to enhance the power generation. Due to the low liquid air yield, a large amount of compression heat is wasted. In order to improve the round-trip efficiency (RTE) and extend the application field, a novel combined cooling, heating and power system based on the LAES (LAES-CCHP) is proposed and investigated. In the proposed system, an organic Rankine cycle (ORC) is employed to recover the high-temperature surplus compression heat to generate electricity and an absorption refrigeration system (ARS) is introduced to utilize the low-temperature compression heat to realize district cooling and heating. Based on a mathematical model, performance evaluation and exergy analysis of the system is performed. It is found that the effective and cascaded utilization of the compression heat could significantly improve the efficiency and performance of the system. With optimal operational parameters, the RTE and exergy efficiency of the LAES-CCHP could reach 69.64% and 57.02%, respectively, which are 37.66% and 12.71% higher than those of the B-LAES.

Energy, exergy, economic, and environmental (4E) analyses and optimization of a CCHP system with steam turbine

AbstractDue to finding solutions to reduce energy consumption and improve the electricity production efficiency in the power cycles, using the waste heat of power cycles in the combined generation systems can be a useful approach. Also, by using the wasted energy of the power generation cycles, the cooling and heating demands can be met. Therefore, this study presents energy, exergy, economic, and environmental evaluations of a new configuration of combined cooling, heating, and power (CCHP) or tri‐generation system with a steam turbine. This CCHP system includes a steam boiler, a steam turbine, a heat exchanger, a single‐effect absorption chiller, and pumps. Some important performance parameters such as electrical efficiency, combined electrical and cooling efficiency, combined electrical and heating efficiency, and tri‐generation efficiency, as well as exergy efficiency, are considered. Also, various output and environmental performance parameters such as percentage of operational cost reduction, percentage of fuel consumption reduction, percentage of pollutant emission reduction, and economic and technical issues based on different inlet temperatures and pressures of the steam turbine are investigated. In addition, the performance of the absorption chiller at different operating conditions has been investigated. This study shows that the usage of the CCHP system leads to reduction of 24.91% in carbon dioxide emission, 15.83% in fuel consumption, and 35.34% in operating costs compared with conventional systems. Also, the results show that there is a good improvement in the overall performance of the system with the tri‐generation efficiency of 82.46%.

Sustainability Analysis of Green Data Centers With CCHP and Waste Heat Reuse Systems

Data centers worldwide consume a large amount of energy and pose serious threats to the environment. Combined cooling, heating, and power (CCHP) and waste heat reuse (WHR) systems are two effective approaches to improve the data center energy efficiency. However, whether and to what extent the combination of them can bring further performance improvement have not been explored yet. Moreover, it is unclear how to manage the energy flow in such a hybrid system. In this article, we try to answer these questions by proposing a data center energy architecture that includes both CCHP and WHR technologies. We formulate the energy management problem as two optimization problems with different objectives. By analytically solving the optimization problems, two energy management strategies, namely, Following the electrical load with waste heat reuse (FEL-WHR) and operating cost-aware energy management with waste heat reuse (OCM-WHR), are developed. We evaluate the performance of proposed solutions by extensive simulations using a real-world workload trace from a production data center. Numerical results suggest that the CCHP and WHR technologies generally reduce the operating cost and are more environmental friendly. If the power generation efficiency of CCHP system is high, the impact of WHR will be more conspicuous (up to 8 percent improvement) and FEL-WHR is recommended because it achieves better sustainability at an operating cost comparable to OCM-WHR. Otherwise, WHR only improves the sustainability trivially, and the energy management policy should be carefully chosen according to the data center operator's objective.

Thermodynamic Analysis of Combined Cooling Heating and Power System with Absorption Heat Pump Based on Small Modular Lead-based Reactor

Small Modular Lead-based Reactor (SMLR) has generated great interest in academic research all around the world due to its good safety characteristics and relatively high core outlet temperature. In this paper, a Combined Cooling Heating and Power (CCHP) system with usage of absorption heat pump, which couples with a SMLR, was proposed to fulfill the energy demands in remote areas. Thermodynamic analysis was implemented to improve the performance of the CCHP system based on SMLR. To meet the remote areas’ energy needs, the main parameters and mass flow rate of a 35 MWth SMLR design were analyzed. The SMLR CCHP with absorption heat pump system can provide electric power 12.5MWe, heating 9.5MWh, and cooling 2.54MWc. The total energy utilization efficiency of the system can be 69.12 %. This work can provide a reference in the design and optimization of the CCHP system to meet the energy demands in the remote areas.

Optimal Energy Scheduling for Data Center With Energy Nets Including CCHP and Demand Response

Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.

Design of a multi-energy system under different hydrogen deployment scenarios

Multi Energy Systems (MES) are effective means to increase Renewable Energy Sources (RES) penetration in the energy system and therefore to move toward a decentralized low-carbon system. Several energy vectors can be integrated together to exploit synergies in a MES framework, such as electricity, heat and hydrogen. The latter is one of the most promising energy carriers to promote widespread use of MES. Predictive management and well-defined sizing methodology are mandatory to achieve maximum performance out of MES. In this study a grid-connected MES consisting of a photovoltaic (PV) plant, a Battery Energy Storage System (BESS) and a Proton Exchange Membrane Fuel Cell (PEMFC) as a programmable Combined Cooling Heat and Power (CCHP) source, is modelled. Natural gas is considered as an alternative fuel to pure hydrogen. Mixed Integer Linear Programming and Genetic Algorithm are used respectively to solve operation and sizing problems. A single-objective optimization approach, including emission factors as optimization constraints, is carried out to find the optimal configuration of the MES. Several future scenarios are studied, considering different percentages of hydrogen in the gas mixture and comparing the techno-economic performance of the system with respect to a pure hydrogen fueling scenario. Results showed that the environmental objective within the design optimization, promote the use of hydrogen, especially in scenarios with high share of green hydrogen.

Operation Strategy Analysis and Configuration Optimization of Solar CCHP System

This paper proposed a new type of combined cooling heating and power (CCHP) system, including the parabolic trough solar thermal (PTST) power generation and gas turbine power generation. The thermal energy storage subsystem in the PTST unit provides both thermal energy and electrical energy. Based on the life cycle method, the configuration optimization under eight operation strategies is studied with the economy, energy, and environment indicators. The eight operation strategies include FEL, FEL-EC, FEL-TES, FEL-TES&EC, FTL, FTL-EC, FTL-TES, and FTL-TES&EC. The feasibility of each strategy is verified by taking a residential building cluster as an example. The indicators under the optimal configuration of each strategy are compared with that of the separate production (SP) system. The results showed that the PTST-CCHP system improves the environment and energy performance by changing the ratio of thermal energy and electric energy. The environment and energy indicators of FEL-TES&EC are superior to those of FTL-TES&EC in summer, and the results are just the opposite in winter. The initial annual investment of the PTST-CCHP system is higher than that of the SP system, but its economic performance is better than that of the SP system with the increase of life-cycle.

Evaluation of Integrated Energy System in Airports Based on Comprehensive Weighting Method

Airport is a typical integrated energy system in a park with various energy requirements. In this paper, a multi-dimensional quantitative analysis of system performance indicators was conducted by using a comprehensive weighting method based on the analytic hierarchy process (AHP) and anti-entropy weight method. A distributed energy system evaluation matrix model was used to evaluate and compare different integrated energy designs. The results showed that electric boilers would increase the primary energy ratio and primary energy consumption than the ones caused by gas boilers. Also, energy storage devices could significantly decrease pollutant emissions of integrated energy systems but would increase investment costs and reduce the economic indicators of system solutions. In a word, the configuration with ice storage, combined cooling, heating and power (CCHP), gas boiler, ground source heat pump (GSHP), air source heat pump (ASHP), and absorption chiller had the best evaluation indicators.

A Reliability-Based Optimization Model for Operational Management of CCHP System

The combined cooling, heating, and power (CCHP) system has been explored extensively due to its ability to reduce the carbon dioxide emission and improve the energy-utilization efficiency. However, the existing studies associated with CCHP system rarely concerned for the system reliability, although it was capable of enhancing the stability of operational patterns. In this study, predefined reliability coefficient (i.e., r) was incorporated innovatively into a CCHP system optimization model in order to examine the influence of reliability level on model results. A variety of solutions under different r values were obtained, which effectively reflected the trade-off between system economy and reliability. The CCHP system of a hotel in Shanghai, China, was used as a study case for demonstration. The generated results indicated that the system cost would increase with the increase of reliability level; meanwhile, the user requirements in cooling, heating and electricity were ensured greatly. The successful application of proposed optimization model in real case is expected to be a good example for CCHP system management.

Techno-economic Optimization of Combined Cooling, Heat and Power System Based on Response Surface Methodology

In the present work, the statistical analyses are presented to study the economic indexes of Net Present Value (NPV) and Simple Payback Period (SPB) as response functions for the Combined Cooling, Heating and Power (CCHP) system. The CCHP performance is simulated with the aid of thermodynamic modeling, and also economic equations are presented for economic simulation. An attempt is made to study the effect of some economic factors (interest ratio, fuel cost, lifetime, and electricity sell price) on the system’s responses. Based on the Design of Experiment analysis, regression models are presented to quantify the effects of these parameters on the Net Present Value and Simple Payback Periods. This novel approach is developed utilizing the response surface methodology (RSM) based on the central composite design (CCD) method. Sensitivity analysis of the economic parameters was also examined in this research. Optimal values of these parameters were obtained for the two economic indexes as response functions.

Novel energy station by natural fluid CO 2

Novel energy station by natural fluid <scp> CO ₂ </scp>

Multi-objective optimization of CCHP system with hybrid chiller under new electric load following operation strategy

The performance of combined cooling, heating and power (CCHP) system is greatly affected by its operating strategy and design. In this paper, a new electric load following (NELF) strategy was developed. It is based on the alternation between absorption cooling and electric cooling according to the building energy requirements, for hybrid chiller based CCHP systems. A comparison of the new proposed strategy with the modified electric load following (MELF) and electric load following (ELF) strategies is performed. A multi-objective optimization approach based on genetic algorithm is carried out to predict the optimal capacity of CCHP systems. Performance criteria like primary energy consumption, annual total cost and carbon dioxide emission were considered as objective functions. The performances of these CCHP systems and operation strategies were examined and compared with the separated production (SP) system for a Mosque complex located in Algiers, Algeria. Results show that hybrid chiller CCHP based NELF strategy is the best choice, which can reduce the primary energy consumption by 34.45 GWh/year, annual total cost by 0.313 million €/year and carbon dioxide emission by 8.37 kton/year. Compared to the other configurations and strategies, the hybrid CCHP based NELF achieves better energetic, economic and environmental performance under the optimized conditions.

Research on Comprehensive Value of Electrical Energy Storage in CCHP Microgrid with Renewable Energy Based on Robust Optimization

The combined cooling, heating, and power (CCHP) microgrid system is good for energy gradient utility. At the same time, it can promote the renewable energy (RE) consumption and abate environmental pollution. In a CCHP microgrid system, the electrical energy storage (EES), which can storage and release electrical energy, plays an indispensable role. A robust optimization model of the CCHP microgrid participating in power market transaction is constructed to calculate the CCHP microgrid operation cost in 4 cases. The results show that the EES can significantly reduce the cost of the CCHP microgrid by 13.21%, compared with 8.36% in Group 1 without renewable energy. The EES can reduce the reserved capacity of micro gas turbine units to deal with the precariousness of RE generation and then reduce the CCHP microgrid operation cost by reducing the purchase of energy from the power grid and arbitrage. Finally, the calculation method of comprehensive value of the EES is constructed. The comprehensive value of the EES is higher in Group 2 with renewable energy compared with Group 1 without renewable energy. Through net present value (NPV) calculation and sensitivity analysis, it is found that the RE penetration level and EES cost have the greatest impact on the economic performance of EES. This shows that with the continuous rising of the RE penetration level and the gradual decrease of EES cost, great potential still waits to be tapped in the comprehensive value of EES in the future.

Stochastic optimal operation of a microgrid based on energy hub including a solar-powered compressed air energy storage system and an ice storage conditioner

Energy Hub (EH) is known as a complex system capable of transferring, conversion, and saving various energies in a Microgrid (MG). An important issue for investors is taking advantage of EH for optimal management of all energy carriers, particularly in anticipating energy price. In this paper, a model for EH was considered for the purpose of the optimal operation of the MG with multiple energy carrier infrastructures for day-ahead. The goal of optimization was to minimize operation and environmental costs subjected to numerous technical constraints. The proposed EH manages dispatchable generation, i.e. Combined Cooling, Heat and Power (CCHP) and non-dispatchable generations, i.e., Wind Turbine (WT) and Photovoltaic (PV). It considers an Ice Storage Conditioner (ISC) as well as a Thermal Energy Storage System (TESS) as the Energy Storage System (ESS). In particular, the effects of Solar-Powered Compressed Air Energy Storage (SPCAES) as a novel ESS were studied on the performance and efficiency of the EH operation and environmental costs. The suggested model considers the stochastic behavior of WT and PV generations as well as the electrical, thermal, and cooling demands in various scenarios. The scenario generation was performed while the K-means clustering algorithm was applied for reducing the number of scenarios. The proposed model was a Mix Integer Linear Problem (MILP), which was solved using CPLEX solver in GAMS software. Implementation of the proposed framework on the typical EH showed the efficacy of the ESSs to reduce the operation costs and emissions in the day-ahead energy management.