Combined Cooling, Heating And Power Research Articles

Random decision forest (RDF) and crystal structure algorithm (CryStAl) for uncertainty consideration of RES & load demands with optimal design of hybrid CCHP systems

This manuscript proposes a multi-objective random development mode of hybrid Combined Cooling, Heating, And Power (CCHP) system, supply configuration, enclosing a turbine, cooler/heater, battery and storage tank, and photovoltaic/thermal collectors. The proposed optimal strategy is consolidation of Random Decision Forest (RDF) and Crystal Structure Algorithm (CryStAl), hence it is called RDF-CryStAl technique. The Power center models of power converters along with storage devices are create by assuming the characteristics of non-design elements. The annual value rate falls when system confidence levels drop and uncertainty is used. The yearly value savings rate is shown to be highly sensitive to the price of fossil-fuels as a result of the sensitivity analysis of economic-frontiers based on important economic factors. As a result, the inversion of star-collectors includes a robust impact of the turbine.

Optimal stochastic scheduling in residential micro energy grids considering pumped-storage unit and demand response

Improving energy efficiency, production costs, greenhouse gas emissions, and reliability are some of the most important problems in the energy industry. Energy storage systems and demand-side management (DSM) programs are among the existing solutions for removing such problems. This study presents an improved micro energy grid (MEG) to reduce operating costs as well as greenhouse gas emissions using combined cooling, heating, and power (CCHP) systems, wind turbines (WTs), photovoltaic (PV) units, pumped-storage units, and heating and cooling storage units. In this system, pumped-storage units are employed to store energy. Price- and incentive-based programs are employed to obtain better performance on the demand side. Uncertainties of wind speed, solar power, electricity price, and electrical load are considered to achieve more accurate results. In this paper, the Latin hypercube sampling (LHS) method is applied to generate various scenarios. Then, K-means clustering approach is employed to reduce the number of scenarios. By producing and reducing the proposed scenario, the inherent uncertainty in renewables can be covered, which will result in a more efficient model. Meta-heuristic whale optimization algorithm (WOA) and Pareto model are used to solve single- and multi-objective functions and to optimally design the proposed structure considering contradictory functions, respectively. The features of the proposed developed algorithm are simplicity in execution and absence of derivative operator, increase of local and global search capability and consideration of objective functions with different nature. Moreover, fuzzy decision-making mechanism is proposed to select the optimal solution from the Pareto set of solutions. Finally, the proposed approach in different scenarios on the microgrid with various sources is discussed. The results indicate the proposed method properly obtains the optimal solution.

Multi-dimension day-ahead scheduling optimization of a community-scale solar-driven CCHP system with demand-side management

Demand-side management (DSM) can transfer the dynamic fluctuations of load to match the renewable energy sources and realize the interaction of source-load. In most existing combined cooling heating and power (CCHP) system scheduling, user-side and system-side resources are independently optimized to achieve optimum economic performances without efficiently combining resources. In this paper, the devices with adjusted loads on the system-side and the user-side are integrated to construct generalized energy storage (GES) model, including traditional flexible resources, batteries, electric vehicles, cooling and heating load response, and electro-to-thermal conversion devices. Then, the GES model is integrated into the system for optimization including economy, load fluctuation, energy efficiency, and exergy efficiency. A case study demonstrates the application of the proposed method. The scheduling results of four DSM cases under different objectives are comprehensively obtained and compared. The optimized schemes are assessed in multi-dimension decision-making method and the effects of objective weights on decision results are analyzed. The simulation results illustrate that the load variation is decreased by 54% to obtain a smooth electrical load, resulting in the stable operation of equipment and improving the security and stability of the CCHP system. The exergy efficiency, energy efficiency, and economy performances through the DSM optimization are improved by up to 8.7%, 2.5%, and 2.4%, respectively. The system optimized with the objective of load fluctuation achieves the highest score in the TOPSIS decision-making scenarios and has the best comprehensive performance.

Energy, exergy and economic analysis for a combined cooling heat and power system: A case study for a university campus

Efficient power generation systems are required because of the rapid increase in demand for energy in industry, commerce, and residence. Systems that produce power and heat can have higher efficiency. In this work, a Combined Cooling Heat and Power (CCHP) system with a gas turbine is used to produce electric power for chillers and hot water for a university campus. The current energy plant for the university is described, showing the annual cost for each sub-system, and the cooling and heating demands. Five gas turbines are analyzed and used with a heat exchanger to produce hot water in the CCHP system. A thermodynamic model has been developed based on the parameters given by gas turbine manufacturers for the energy, exergy, and economic analysis. The thermodynamic and exergy calculations are compared with literature to verify the results. The difference between the company data and the thermodynamic model is 6.9% for turbine outlet temperature, and 0.3% for thermal efficiency. The average exergetic efficiency of the proposed energy plant is 45%. Siemens – 5.4 MW has the lowest payback and the highest ROI with 2.54 and 7.48%, respectively. The annual fuel cost for the Solar – Taurus 60 is US$1.2 million.

Optimal design of a multi-generation system based on solar and geothermal energy integrated with multi-effect distillatory

In this study, an optimal system is designed to meet the needs of fresh water, heating, cooling and electricity. Four modes were simulated in MATLAB to provide the requirements of a hotel. The combined cooling, heating and power (CCHP) with multi-effect distillation through thermal vapor compression (MED-TVC) was used as the base mode. Geothermal wells (Geo) were employed to convert the renewable energy of the earth into heat and electricity. Also, the flat plate collectors (FPC), photovoltaic panels (PV) were utilized to gain the sun's radiant energy and convert it into heat and electricity. An organic Rankine cycle (ORC) has been used to convert the obtained geothermal energy into electricity. The genetic algorithm has been used for optimal design. In the case of base, base + Solar, base + Geo and base + Solar + Geo 21, 24, 24, 27 design parameters were considered respectively. The total annual cost (TAC), in which the effects of pollution were included, was considered as the objective function in the optimal design. As the innovation of this work, the use of geothermal and solar energy with desalination process combined with CCHP was mentioned, which was not done in previous works. For a case study, the annual needs of a hotel in Bandar Abbas, Hormozgan province, Iran were considered and the TAC obtained results for base, base + Solar, base + Geo and base + Solar + Geo modes were 1.2789×106$/year, 1.0950×106$/year, 5.8048×105$/year and 5.1436×105$/year , respectively. It was found that in the mode of using solar energy, geothermal energy and solar and geothermal energy, the objective function was improved 14.38%, 54.61% and 59.78%, respectively, comparing to the base mode.

The analysis of time-sharing economic exergy efficiency of the CCHP system using planetary search algorithms

CCHPs (Combined Cooling, Heating, and Power Systems) are capable of providing cold energy, heat, and electricity to users, allowing cascading utilization of energy and improving energy efficiency. The imbalance between cooling, heating, and electrical energy makes it difficult to accurately evaluate the performance of a CCHP system. Existing indices for evaluating the performance of the CCHP system do not account for the influence of time-sharing tariffs; therefore, the quantitative index of time-sharing is added and the time-sharing economic exergy efficiency of the radiator is established. Given that the electrical and thermal characteristics of the advanced absolutely hot compressed airheat storage system (AA-CAES) can complement the CCHP system, a model of the CCHP system with AA-CAES is established, which can be used to validate the validity of the quantitative evaluation index of time-sharing. A planetary search algorithm is proposed for solving the CCHP system model to address the multi-parameter solving characteristics of the CCHP system model and the disadvantages of the existing multi-objective optimization algorithms, which are prone to local optimality and poor optimization accuracy. Simulation validation demonstrates that the time-sharing economic exergy efficiency proposed in this paper can more accurately reflect the total energy consumption of the CCHP system than the existing evaluation indices. The performance of the CCHP system can be improved by using AA-CAES as a heat storage device.

Multi-objective optimization and improvement of multi-energy combined cooling, heating and power system based on system simplification

The multi-objective optimization of combined cooling, heating, and power (CCHP) systems typically focuses on optimizing objective functions that consider energy, environmental, and economic factors. However, the implementation of optimization solutions, which is related to decision variables, is often overlooked. This paper proposes a novel improved optimization method based on system simplification by reorganizing, re-optimizing and re-selecting the initial optimized population to improve decision variables. And created algorithm pseudocode. The improved algorithm is adopted to perform multi-objective optimization on the established multi-energy CCHP system. The hypervolume of the improved/original algorithm is 0.9770/0.9887, with only a 1.18% difference. The optimization results of the improved algorithm and the original algorithm are equal in the minimum values of the net present value (NPV) and fossil energy consumption (FEC), which are 2.26 × 107$ and 1.21 × 105GJ, respectively. There is only 1.4% difference in the carbon dioxide emissions (CDE). The number of equipment types corresponding to the decision variables of the improved algorithm is reduced from 8 to 4, significantly simplifying the systems. The proposed improved optimization method can harness the local energy-saving potential and comprehensively analyze optimization solutions in terms of energy, economy, and environment. It also obtains simplified systems to promote the implementation of the project.

Multi-objective evaluation and operation strategy analysis for combined cooling, heating and power (CCHP) systems

Multi-objective evaluation and operation strategy analysis for combined cooling, heating and power (CCHP) systems

Thermodynamic and economic analysis of a multi-energy complementary distributed CCHP system coupled with solar thermochemistry and active energy storage regulation process

The combination of distributed energy systems (DES) and solar energy is considered a vital measure to save the usage of fossil energy. A new distributed combined cooling, heating and power (CCHP) system integrated with solar thermochemistry (STC) and energy storage (ES) units is proposed. The methane steam reforming (MSR) reaction is driven by solar energy to produce hydrogen-rich syngas, which is fed with high-efficiency solid oxide fuel cell (SOFC) – micro gas turbine (MGT) to produce electricity. The residual heat of the flue gas drives a double effect lithium bromide unit for cooling and a heat exchanger for heating, while a parabolic trough collector (PTC) is used to supplement cooling and heating supply. Under different weather conditions and user load demands, simulation analysis of the matching between system outputs and user loads is conducted, and the thermodynamic and economic performance are analyzed. The results show that through active energy storage regulation, the new system outputs can meet the hourly user loads with a matching degree of 1. The total exergy efficiencies of the system on typical summer, transition and winter days are 69.93%, 62.03%, and 52.28%, respectively, and the specific CO2 emission rates are 152.57 g/kWh, 191.75 g/kWh, and 234.16 g/kWh, respectively, indicating that the system has higher exergy efficiency and is more environmentally friendly under strong solar radiation. Economic analysis indicates that the capital investment cost is 779.34 k$, and the revenue begins in the 9-th year.

Bioenergy production from pretreated rice straw in Nigeria: An analysis of novel three-stage anaerobic digestion for hydrogen and methane co-generation

The response to Nigeria's energy inadequacies and waste management issues is projected in this research to be achieved with hydrogen and methane co-produced from pretreated (PT) rice straw (RS) in a three-stage digestion process. This study also demonstrated a novel pretreatment (PTM) agent to improve biological energy recovery from RS. The objectives were accomplished using acidogenic and methanogenic procedures in batch, semi-continuous and continuous systems after pretreating RS with chemical/potash extract (PE) followed by a biological agent. At the same time, the energy assessments were done using data from the laboratory study and the literature. The research findings indicated that at the acidogenesis stage, specific hydrogen yield was insignificant when chemical agents and PE were employed alone. However, the daily H2 production increased when the PT RS residues were enzymatically hydrolysed with NaOH-PT (114 NmL H2 g−1 TS d−1) and PE-PT (103 NmL H2 g−1 TS d−1) RS substrates having the highest values at steady states and raw RS producing the least (30 NmL H2 g−1 TS d−1). In the methanogenesis phase, chemical/ PE PTM followed by enzymatic hydrolysis of RS improved the daily specific methane production by 18%, 31.7% and 41.5% for HCl, PE and NaOH-PT RS residues. The methane production efficiency was 80% for NaOH, 75% for PE, and 68% for HCl RS PT samples, while the raw RS was 48%. The total output energy expressed in electricity and thermal production using combined cooling, heat, and power (CCHP) showed that NaOH and PE-PT RS digesters gave the highest electricity (892.43 and 852.00 KWhelect. tonne-1 TS) and thermal (1194.10 and 1140 KWhtherm. tonne-1 TS) yield respectively. Similarly, the cooling fluid in KWhcool produced per tonne of TS RS was 835.57, 798.00 and 741.66 for NaOH, PE and HCl PT RS samples. Finally, whereas the Firmicutes, especially the Clostridium, Ruminococcus and Thermoanaerobacterium, were the dominant microbial community in acidogenic digestates, the Euryarchaeota typified by Methanobacterium, Methanosarcina and Methanosaeta were the principal phyla for most methanogenic reactors.

Multiobjective optimization of a cogeneration system based on gas turbine, organic rankine cycle and double-effect absorbtion chiller

Combined cooling, heating and power (CCHP) is one of methods for enhancing the efficiency of the energy conversion systems. In this study a CCHP system consisting of a gas turbin (GT) as the topping cycle, and an organic Rankine cycle (ORC) associated with double-effect absorbtion chiller (DEACH) is decisioned as the bottoming cycle to recover the waste heat from GT exhaust gas. The considered CCHP system is investigated to maintain electricity, heating and cooling demand of a town. A parametric study is investigated and the effect decision variables on the performance indicators including exergy efficiency, total cost rate (TCR), cooling capacity, and ORC power generation is examined. Decision variables of the ORC system consist of HRVG pressure, and condenser pressure and the DEACH including evaporator pressure, condseser pressure, concentration of the concentrated solution, concentration of the weak solution, and solution mass flow rate. Finally a multi-objective optimization performed using Genetic Algorithm (GA) and the optimal design point is selected. It is observed at the optimum point the exergy efficiency, TCR, and sustainability index are 17.56%, 74.49 $/h, and 1.21, respectively.

A MOEA/D approach using two crossover strategies for the optimal dispatches of the combined cooling, heating, and power systems

In this paper, one MOEA/D variant using two crossover strategies (MOEA/D-TCS) and two constraint handling methods are proposed to deal with four combined cooling, heating, and power (CCHP) scenarios in summer and winter. MOEA/D-TCS maintains a balance between global search and local search by combining a modified spiral updating position of whale optimization algorithm and the simulated binary crossover of genetic algorithm, and it is able to obtain a wide and even Pareto set for each CCHP scenario. The two constraint handling methods enable infeasible solutions to get rid of infeasible regions over a short period of time, guaranteeing that the reserved solutions of MOEA/D-TCS satisfy all constraints. Experimental results show that each CCHP scenario with the battery has lower energy consumption, operation cost and carbon dioxide emission than the corresponding CCHP scenario without the battery, and hence the battery is helpful to the improvements of the flexibilities and efficiencies of energy cascade utilization for the CCHP systems in summer and winter. Also, MOEA/D-TCS performs better than the other seven multi-objective evolutionary algorithms and achieves larger hypervolumes and coverage rates for four CCHP scenarios.

Analysis of multi-cascade CCHP system with gas turbine bypass extraction air energy storage

The operational load of gas turbine can be regulated by using compressor bypass extraction. In this paper, a combined cooling, heating and power (CCHP) system integrating gas turbine cycle (GTC), compressed air energy storage (CAES), supercritical CO2 Brayton recompression cycle (SCRC), organic Rankine cycle (ORC) and absorption refrigeration cycle (ARC) is proposed. The use of CAES not only enables flexible recovery of waste heat and load demand regulation, but also enables effective storage and secondary use of bypass extracted air from the compressor. The temperature of the graded expanded air is reduced and returned to the gas turbine unit, improving its thermal performance. The exergy and economic analyses show that the GTC has the largest percentage of both exergy destruction and investment cost rates, over 60%. Under the proposed operation model, the payback period of a single gas turbine is 4.12 years, and the payback period of the proposed CCHP system is 2.44 years. Thermodynamic, economic and environmental evaluation models are established to analyze the system performance at different loads. At the lowest load operation, the energy efficiency of the CCHP system can reach 75.99%, the exergy efficiency is 45.89%, the unit energy cost is 0.032 $/kWh, and the greenhouse gas equivalent emission is 0.313 kgCO2e/kWh. Compared with the other four cascade waste heat recovery systems, the proposed CCHP system greatly improves the thermodynamic performance and reduces the unit energy cost rate and greenhouse gas equivalent emissions, especially under low load operation conditions.

Optimal integration of CCHP with electric Vehicle parking lots in energy hub

Nowadays, due to environmental problems and the lack of fossil fuels, the use of Electric Vehicles (EVs) is extended. These systems need optimal planning and management, like all energy tools. With another look at electrical generation systems, the combined energy production units have also increased dramatically, with only one kind of fuel, it is possible to produce any kind of energy such as electrical, thermal, and cooling energies at the same time. This production type is called the energy hub. The main goal of creating an energy hub is to meet several types of demand at the same time and to increase the flexibility of the system. The Combined Cooling, Heat, and Power (CCHP) unit, which has a higher efficiency ratio to traditional systems, could be referred to as the main component of the energy hub. To reduce the losses of these systems, their planning could be implemented in coordination with the EV parking lot. The energy hub system could provide the mentioned conditions in the presence of renewable energy sources and energy storage units in a coordinated and optimal way. In this paper, the presence of an EV parking lot in an energy hub is investigated that includes a CCHP, a photovoltaic unit in the presence of demand response programs, and its effects on optimizing power consumption to reduce cost. The demand response programs are used to reduce the operating costs of the energy hub. Moreover, the uncertainties of EV consist of entry and exit times and charge level, solar irradiation, and loads value are applied in the model. Finally, the cost of risk by the conditional value–at–risk method is investigated. The simulations are done by determining 3 various case studies for better investigation of the proposed model. The results show that the proposed solution method of the demand response program could decrease the total cost of the energy hub by about 1.90 % in both summer and winter, under risk-averse conditions.

Energy exergy and economic evaluation of a CCHP configuration powered by CPVT collectors dynamically

This study presents a dynamic simulation of a combined cooling, heating, and power (CCHP) configuration powered by concentrating photovoltaic thermal (CPVT) collectors. The desired configuration comprises CPVT collectors, a phase change material (PCM) storage tank, an organic Rankine cycle (ORC), an absorption chiller, and heat exchangers. The evaluation has been carried out for a residential building using EES, MATLAB, and TRNSYS software. Moreover, an auxiliary heater has been used to provide the thermal energy of the desired configuration in case of low or no solar irradiation. The desired configuration has been evaluated from energy, exergy, and economic points of view during the warm months of the year (May to September). Also, the influences of key parameters of the desired system such as the CPVT area, CPVT flow rate, and PCM melting temperature on the performance of the desired configuration have been examined. The results indicate that the solar fraction enhances by about 161 % when the CPVT area changes from 100 m2 to 400 m2. Also, CPVT has found to be the main contributor to exergy destruction, accounting for 49% of the total. The maximum energy and exergy efficiencies of the desired configuration are obtained at the CPVT area of 400 m2 with values of 72.26 % and 79.03 % respectively. On the other hand, the lowest payback period occurs at 100 m2 with value of 11 years.

Techno-economic-environmental performance of multi-stage evaporation based poly-generation system under multiple energy supply scenarios

To fulfil the seasonal energy demand in buildings, a novel thermally-driven poly-generation system is proposed based on organic Rankine cycle (ORC) and two-stage vapor compression cycle (TSVCC). The thermodynamic model of the combined cooling, heating and power (CCHP) system was constructed based on the laws of thermodynamics and energy-supply scenarios were also analyzed. Techno-economic-environmental performance of CCHP system was analyzed and compared with the traditional standalone energy-supply modes. Results show that the heating capacity of novel poly-generation system is at least 11% higher than single heating system. For the power generation performance, the novel poly-generation system is almost the same with the traditional ORC. The cooling capacity of poly-generation system is 8% worse than absorption refrigeration system due to the relatively low efficiency of ORC. The poly-generation system can meet the seasonal demand in buildings, the overall performance becomes more attractive with a rising heat source temperature, optimal operating parameters and proper working fluid. The net power output is 3199.37 kW and payback period will lower than 3 years if the heat source temperature is 473.15 K and R1233zd as the working fluid. Therefore, the proposed poly-generation system is excellent and should be employed in engineering applications.

Reliability, availability, and life-cycle cost (LCC) analysis of combined cooling, heating and power (CCHP) integration to data centers considering electricity and cooling supplies

The utilization of digital technology is increasing rapidly and it is causing a growth in the number of data centers around the world. Data centers provide essential services, making them known as mission-critical. Like other mission-critical facilities, data centers often use reliability and availability analysis to evaluate the level of safety of their operations. Reliable electricity and cooling supplies are vital for the data center’s operation, and almost all data centers rely on the power grid to feed their electrical and cooling systems. However, the power grid is considered an unreliable source, and power grid fragility is a raising concern among data center operators. An alternative to the power grid is a sustainable combined cooling, heating, and power (CCHP) system, which can supply continuous electricity and cooling to a data center while providing local energy generation, energy cost saving, and emission benefits. To understand the impact of a CCHP integration on the reliability and availability of a grid-fed data center, an up-to-date electrical topology design and reliability block diagram models were created that take into account both electricity and cooling supplies. The results showed that the overall system reliability of a grid-fed data center increased from 49.23% to 74.62% with a CCHP integration. In terms of availability, a grid-fed data center with 2N redundant topology achieved the industry-accepted high availability level of 0.99999, also known as 5-nines. Moreover, the use of CCHP at the grid-fed data center increased the availability from 5-nines to 7-nines for the overall system. Thus, CCHP integration to a grid-fed data center provided higher reliability and availability for the overall system for even 2N redundant topology data centers. Moreover, based on the life-cycle cost (LCC) analysis, integrating CCHP systems into data centers has a promising outcome. However, when conducting an LCC analysis, we recommend considering the realistic cost of downtime along with the reliability-related exposure to properly assess the viability of alternative energy systems by data center investors and operators.

Modeling and Optimization of Natural Gas CCHP System in the Severe Cold Region

A natural gas combined cooling, heating, and power (CCHP) system is a typical integrated energy supply method that optimizes end−use energy. However, how to achieve economically feasible natural gas CCHP in severe cold regions with low−grade heat demand reaching 50% is still a pressing issue. This paper establishes a typical natural gas CCHP system model for severe cold regions and conducts the system. Based on the climate conditions of Harbin, the economic optimization of independent gas turbine systems, internal combustion engines, and gas turbine systems is still a pressing issue. Based on the climate conditions of Harbin, the economic optimization of independent gas turbine systems, internal combustion engine systems, and steam boiler systems under different cooling and heating load ratios was carried out. The combination of “internal combustion engine + steam boiler” has the most optimal cost of RMB 1.766 million (USD 0.255 million), saving 10.7%, 7.8%, and 18.3% compared to the three single equipment subsystems respectively. This provides good theoretical support for the construction of multi−energy heterogeneous energy systems.

Chance-constrained optimization of hybrid solar combined cooling, heating and power system considering energetic, economic, environmental, and flexible performances

Uncertain parameters and factors substantially impact the operational performances of combined cooling, heating, and power (CCHP) systems. This paper constructs a chance-constrained programming model for system design and energy dispatch management of a hybrid solar CCHP system under the uncertainties of solar energy and building loads. The uncertain scenarios with probability distributions are generated in the Latin hypercube sampling and clustering methods. The chance-constrained model transforms stochastic optimization into deterministic optimization based on these scenarios. Then, the multi-objective optimization model of CCHP system considering the performances of economic, energy, emission, and flexibility is established, and the modified ε-constraint method is employed to obtain Pareto solutions. A case study validates the proposed model. The capacities and operational strategies of components in the hybrid CCHP system are optimized, and the impacts of the confidence level of probability distributions of uncertain factors on the optimization results are discussed. The results indicate that the photovoltaic capacity in the hybrid CCHP system declines by 87.5% when the confidence level increases from 0.50 to 0.99. But other components’ capacities are raised, and the gas turbine capacity as the key component rises by 10.49%. The energetic and environmental performances of the CCHP system rise with the confidence level, and the operation safety is improved.

Distribution grid electrical performance and emission analysis of combined cooling, heating and power (CCHP)-photovoltaic (PV)-based data center and residential customers

As the number of data centers is increasing worldwide, there are concerns about their growing electricity demand on power grids and its impact on the environment. These concerns can be mitigated at the urban scale by utilizing the waste heat generated by data centers and multi-energy hub integration. Using Combined Cooling, Heating, and Power (CCHP) and Photovoltaic (PV) systems within a data center, in combination with district energy network connections, can create energy interactions with residential customers in a multi-energy hub. However, connecting large energy consumers and distributed energy systems to the electrical distribution grid presents challenges due to the conventional design of the power grid with unidirectional electricity flow. There is a lack of understanding about the grid impact of data centers with multi-energy systems regarding electrical performance parameters, energy demand, and emissions. To address these open issues, we developed an hourly energy interaction model that includes a CCHP-PV-based data center and residential customers. In addition, the CCHP-PV-based data center was operated with a cost-optimized energy scheduling. We conducted six case studies to distinguish the specific effects of CCHP, PV, and district energy network connections. The model takes into account hourly outdoor temperatures, energy prices, and varying energy demands to capture the seasonal impact on electrical performance parameters and energy results. From an energy and environmental perspective, CCHP-PV systems can eliminate the data center's electricity demand from the grid, reduce residential energy demand, and decrease carbon dioxide equivalent emissions. This cleaner production and energy exchange further support the United Nations Sustainable Development goals. The experimental results showed a 43% reduction in power loss in the distribution grid and helped the data center's main electrical bus and residential bus against the voltage drop phenomenon. The CCHP-PV-based data center with district heating and cooling connection reduced the peak hour electricity demand on the grid by 39% and annual electricity consumption from the grid by 66%. Furthermore, Greenhouse Gas Protocols (GHG) Scope-2 emissions associated with the data center's grid electricity consumption and only residential electricity consumption were reduced by 77% and 36%, respectively.